A VHDL-AMS Modeling Methodology for Top-Down/Bottom-Up Design of RF Systems

Indo-ChinaAn agreement between Ho Chi Minh and the French (1946) made Vietnam a free state though fighting between parties erupted into the First Indochina War ending in May 1954.Vietnam. (2013). In Encyclopædia Britannica. Retrieved from http://school.eb.com/eb/article-52744GrayscaleForman Safety Negatives, Box

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

A VHDL-AMS Modeling Methodology for Top-Down/Bottom-Up Design of RF Systems

This paper presents a modelling methodology for the top-down/bottom-up design of RF systems based on systematic use of VHDL-AMS models. The model interfaces are parameterizable and pin-accurate. The designer can choose to parameterize the models using performance specifications or device parameters back-annotated from the transistor-level implementation. The abstraction level used for the description of the respective analog/digital component behavior has been chosen to achieve a good trade-off between accuracy, fidelity, and simulation performance. These properties make the models suitable for different design tasks such as architectural exploration or overall system validation. This is demonstrated on a model of a binary FSK transmitter parameterized to meet very different target specifications. The achieved flexibility and systematic model documentation facilitate their reuse in other design projects

Efficient Modelling and Simulation Methodology for the Design of Heterogeneous Mixed-Signal Systems on Chip

Systems on Chip (SoCs) and Systems in Package (SiPs) are key parts of a continuously broadening range of products, from chip cards and mobile phones to cars. Besides an increasing amount of digital hardware and software for data processing and storage, they integrate more and more analogue/RF circuits, sensors, and actuators to interact with their (analogue) environment. This trend towards more complex and heterogeneous systems with more intertwined functionalities is made possible by the continuous advances in the manufacturing technologies and pushed by market demand for new products and product variants. Therefore, the reuse and retargeting of existing component designs becomes more and more important. However, all these factors make the design process increasingly complex and multidisciplinary. Nowadays, the design of the individual components is usually well understood and optimised through the usage of a diversity of CAD/EDA tools, design languages, and data formats. These are based on applying specific modelling/abstraction concepts, description formalisms (also called Models of Computation (MoCs)) and analysis/simulation methods. The designer has to bridge the gaps between tools and methodologies using manual conversion of models and proprietary tool couplings/integrations, which is error-prone and time-consuming. A common design methodology and platform to manage, exchange, and collaboratively develop models of different formats and of different levels of abstraction is missing. The verification of the overall system is a big problem, as it requires the availability of compatible models for each component at the right level of abstraction to achieve satisfying results with respect to the system functionality and test coverage, but at the same time acceptable simulation performance in terms of accuracy and speed. Thus, the big challenge is the parallel integration of these very different part design processes. Therefore, the designers need a common design and simulation platform to create and refine an executable specification of the overall system (a virtual prototype) on a high level of abstraction, which supports different MoCs. This makes possible the exploration of different architecture options, estimation of the performance, validation of re-used parts, verification of the interfaces between heterogeneous components and interoperability with other systems as well as the assessment of the impacts of the future working environment and the manufacturing technologies used to realise the system. For embedded Analogue and Mixed-Signal (AMS) systems, the C++-based SystemC with its AMS extensions, to which recent standardisation the author contributed, is currently establishing itself as such a platform. This thesis describes the author's contribution to solve the modelling and simulation challenges mentioned above in three thematic phases. In the first phase, the prototype of a web-based platform to collect models from different domains and levels of abstraction together with their associated structural and semantical meta information has been developed and is called ModelLib. This work included the implementation of a hierarchical access control mechanism, which is able to protect the Intellectual Property (IP) constituted by the model at different levels of detail. The use cases developed for this tool show how it can support the AMS SoC design process by fostering the reuse and collaborative development of models for tasks like architecture exploration, system validation, and creation of more and more elaborated models of the system. The experiences from the ModelLib development delivered insight into which aspects need to be especially addressed throughout the development of models to make them reusable: mainly flexibility, documentation, and validation. This was the starting point for the development of an efficient modelling methodology for the top-down design and bottom-up verification of RF Systems based on the systematic usage of behavioural models in the second phase. One outcome is the developed library of well documented, parameterisable, and pin-accurate VHDL-AMS models of typical analogue/digital/RF components of a transceiver. The models offer the designer two sets of parameters: one based on the performance specifications and one based on the device parameters back-annotated from the transistor-level implementation. The abstraction level used for the description of the respective analogue/digital/RF component behaviour has been chosen to achieve a good trade-off between accuracy, fidelity, and simulation performance. The pin-accurate model interfaces facilitate the integration of transistor-level models for the validation of the behavioural models or the verification of a component implementation in the system context. These properties make the models suitable for different design tasks such as architecture exploration or overall system validation. This is demonstrated on a model of a binary Frequency-Shift Keying (FSK) transmitter parameterised to meet very different target specifications. This project showed also the limits in terms of abstraction and simulation performance of the "classical" AMS Hardware Description Languages (HDLs). Therefore, the third and last phase was dedicated to further raise the abstraction level for the description of complex and heterogeneous AMS SoCs and thus enable their efficient simulation using different synchronised MoCs. This work uses the C++-based simulation framework SystemC with its AMS extensions. New modelling capabilities going beyond the standardised SystemC AMS extensions have been introduced to describe energy conserving multi-domain systems in a formal and consistent way at a high level of abstraction. To this end, all constants, variables, and parameters of the system model, which represent a physical quantity, can now declare their dimension and associated system of units as an intrinsic part of their data type. Assignments to them need to contain besides the value also the correct measurement unit. This allows a much more precise but still compact definition of the models' interfaces and equations. Thus, the C++ compiler can check the correct assembly of the components and the coherency of the equations by means of dimensional analysis. The implementation is based on the Boost.Units library, which employs template metaprogramming techniques. A dedicated filter for the measurement units data types has been implemented to simplify the compiler messages and thus facilitate the localisation of unit errors. To ensure the reusability of models despite precisely defined interfaces, their interfaces and behaviours need to be parametrisable in a well-defined manner. The enabling implementation techniques for this have been demonstrated with the developed library of generic block diagram component models for the Timed Data Flow (TDF) MoC of the SystemC AMS extensions. These techniques are also the key to integrate a new MoC based on the bond graph formalism into the SystemC AMS extensions. Bond graphs facilitate the unified description of the energy conserving parts of heterogeneous systems with the help of a small set of modelling primitives parametrisable to the physical domain. The resulting models have a simulation performance comparable to an equivalent signal flow model

Design of electronic systems for automotive sensor conditioning

This thesis deals with the development of sensor systems for automotive, mainly targeting the exploitation of the new generation of Micro Electro-Mechanical Sensors (MEMS), which achieve a dramatic reduction of area and power consumption but at the same time require more complexity in the sensor conditioning interface. Several issues concerning the development of automotive ASICs are presented, together with an overview of automotive electronics market and its main sensor applications. The state of the art for sensor interfaces design (the generic sensor interface concept), consists in sharing the same electronics among similar sensor applications, thus saving cost and time-to-market but also implementing a sub-optimal system with area and power overheads. A Platform Based Design methodology is proposed to overcome the limitations of generic sensor interfaces, by keeping the platform generality at the highest design layers and pursuing the maximum optimization and performances in the platform customization for a specific sensor. A complete design flow is presented (up to the ASIC implementation for gyro sensor conditioning), together with examples regarding IP development for reuse and low power optimization of third party designs. A further evolution of Platform Based Design has been achieved by means of implementation into silicon of the ISIF (Intelligent Sensor InterFace) platform. ISIF is a highly programmable mixed-signal chip which allows a substantial reduction of design space exploration time, as it can implement in a short time a wide class of sensor conditioning architectures. Thus it lets the designers evaluate directly on silicon the impact of different architectural choices, as well as perform feasibility studies, sensor evaluations and accurate estimation of the resulting dedicated ASIC performances. Several case studies regarding fast prototyping possibilities with ISIF are presented: a magneto-resistive position sensor, a biosensor (which produces pA currents in presence of surface chemical reactions) and two capacitive inertial sensors, a gyro and a low-g YZ accelerometer. The accelerometer interface has also been implemented in miniboards of about 3 cm2 (with ISIF and sensor dies bonded together) and a series of automatic trimming and characterization procedures have been developed in order to evaluate sensor and interface behaviour over the automotive temperature range, providing a valuable feedback for the implementation of a dedicated accelerometer interface

Etude et modélisation de sources de bruit dans les structures à temps discret

This thesis is dedicated to the noise performance evaluation of the analog and mixed blocks that are located at the beginning of a radiofrequency receiver chain. The main subject of this work is to study a discrete time Sigma Delta modulator, used for analog to digital conversion. The goal is to provide the RF designers, behavioral models capable of evaluating the impact of noise sources on different architectures.That is why we developed a library in VHDL-AMS for discrete time models of noise sources and for behavioral models of analog and mixed blocks (Sigma Delta modulator, RF blocks). We have proved the validity of the noise models by comparing our simulation and the theory. To calibrate the behavioral models, we have performed comparative simulations (transistor model vs. VHDL-AMS model) and measurements on industrial components.The critical point was to estimate the Sigma Delta internal jitter characteristics. This is the reason why we have developed a test circuit, a specific evaluation board associated to an original method. This test method has permitted the internal jitter estimation. We have validated our approach with a global simulation of the physical test bench.Cette thèse concerne l'évaluation des performances en bruit des blocs analogiques et mixtes situés en début d'une chaîne de réception radiofréquence. Ce travail s'attache plus particulièrement à l'étude du modulateur Sigma Delta à temps discret utilisé pour la conversion analogique/numérique. Le but de cette étude est de fournir aux concepteurs de circuits radiofréquence des modèles comportementaux nécessaires à l'évaluation de l'impact des sources de bruit sur certaines architectures.C'est dans cette optique que nous avons développé une bibliothèque en VHDL-AMS comportant des modèles de sources de bruit à temps discret et des modèles comportementaux de circuits analogiques et mixtes (Sigma Delta, bloc RF). Nous avons démontré la validité de la modélisation des sources de bruit par comparaison avec la théorie. Les autres modèles ont été étalonnés à la fois par comparaison avec des simulations au niveau transistor et par comparaison avec des mesures réalisées sur des composants industriels.Le point critique de ces travaux étant l’estimation et la modélisation du jitter interne du modulateur Sigma Delta, nous avons développé conjointement une méthode spécifique de test, un circuit ainsi que sa carte d’évaluation. La valeur mesurée du jitter interne nous a permis de valider l’ensemble de la démarche, grâce à une simulation globale de la chaine en situation réelle

Etude et modélisation de sources de bruit dans les structures à temps discret

Cette thèse concerne l'évaluation des performances en bruit des blocs analogiques et mixtes situés en début d'une chaîne de réception radiofréquence. Ce travail s'attache plus particulièrement à l'étude du modulateur Sigma Delta à temps discret utilisé pour la conversion analogique/numérique. Le but de cette étude est de fournir aux concepteurs de circuits radiofréquence des modèles comportementaux nécessaires à l'évaluation de l'impact des sources de bruit sur certaines architectures. C'est dans cette optique que nous avons développé une bibliothèque en VHDL-AMS comportant des modèles de sources de bruit à temps discret et des modèles comportementaux de circuits analogiques et mixtes (Sigma Delta, bloc RF). Nous avons démontré la validité de la modélisation des sources de bruit par comparaison avec la théorie. Les autres modèles ont été étalonnés à la fois par comparaison avec des simulations au niveau transistor et par comparaison avec des mesures réalisées sur des composants industriels. Le point critique de ces travaux étant l'estimation et la modélisation du jitter interne du modulateur Sigma Delta, nous avons développé conjointement une méthode spécifique de test, un circuit ainsi que sa carte d'évaluation. La valeur mesurée du jitter interne nous a permis de valider l'ensemble de la démarche, grâce à une simulation globale de la chaine en situation réelle.This thesis is dedicated to the noise performance evaluation of the analog and mixed blocks that are located at the beginning of a radiofrequency receiver chain. The main subject of this work is to study a discrete time Sigma Delta modulator, used for analog to digital conversion. The goal is to provide the RF designers, behavioral models capable of evaluating the impact of noise sources on different architectures. That is why we developed a library in VHDL-AMS for discrete time models of noise sources and for behavioral models of analog and mixed blocks (Sigma Delta modulator, RF blocks). We have proved the validity of the noise models by comparing our simulation and the theory. To calibrate the behavioral models, we have performed comparative simulations (transistor model vs. VHDL-AMS model) and measurements on industrial components. The critical point was to estimate the Sigma Delta internal jitter characteristics. This is the reason why we have developed a test circuit, a specific evaluation board associated to an original method. This test method has permitted the internal jitter estimation. We have validated our approach with a global simulation of the physical test bench

A Problem-Oriented Approach for Dynamic Verification of Heterogeneous Embedded Systems

This work presents a virtual prototyping methodology for the design and verification of industrial devices in the field level of industrial automation systems. This work demonstrates that virtual prototypes can help increase the confidence in the correctness of a design thanks to a deeper understanding of the complex interactions between hardware, software, analog and mixed-signal components of embedded systems and the physical processes they interact with

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device