29 research outputs found
The importance of space and time in neuromorphic cognitive agents
Artificial neural networks and computational neuroscience models have made
tremendous progress, allowing computers to achieve impressive results in
artificial intelligence (AI) applications, such as image recognition, natural
language processing, or autonomous driving. Despite this remarkable progress,
biological neural systems consume orders of magnitude less energy than today's
artificial neural networks and are much more agile and adaptive. This
efficiency and adaptivity gap is partially explained by the computing substrate
of biological neural processing systems that is fundamentally different from
the way today's computers are built. Biological systems use in-memory computing
elements operating in a massively parallel way rather than time-multiplexed
computing units that are reused in a sequential fashion. Moreover, activity of
biological neurons follows continuous-time dynamics in real, physical time,
instead of operating on discrete temporal cycles abstracted away from
real-time. Here, we present neuromorphic processing devices that emulate the
biological style of processing by using parallel instances of mixed-signal
analog/digital circuits that operate in real time. We argue that this approach
brings significant advantages in efficiency of computation. We show examples of
embodied neuromorphic agents that use such devices to interact with the
environment and exhibit autonomous learning
Applications in Electronics Pervading Industry, Environment and Society
This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs
Electronic systems for the restoration of the sense of touch in upper limb prosthetics
In the last few years, research on active prosthetics for upper limbs focused
on improving the human functionalities and the control. New methods have
been proposed for measuring the user muscle activity and translating it into
the prosthesis control commands. Developing the feed-forward interface so
that the prosthesis better follows the intention of the user is an important
step towards improving the quality of life of people with limb amputation.
However, prosthesis users can neither feel if something or someone is
touching them over the prosthesis and nor perceive the temperature or
roughness of objects. Prosthesis users are helped by looking at an object,
but they cannot detect anything otherwise. Their sight gives them most
information. Therefore, to foster the prosthesis embodiment and utility,
it is necessary to have a prosthetic system that not only responds to the
control signals provided by the user, but also transmits back to the user
the information about the current state of the prosthesis.
This thesis presents an electronic skin system to close the loop in prostheses
towards the restoration of the sense of touch in prosthesis users. The
proposed electronic skin system inlcudes an advanced distributed sensing
(electronic skin), a system for (i) signal conditioning, (ii) data acquisition,
and (iii) data processing, and a stimulation system. The idea is to integrate
all these components into a myoelectric prosthesis.
Embedding the electronic system and the sensing materials is a critical issue
on the way of development of new prostheses. In particular, processing
the data, originated from the electronic skin, into low- or high-level information
is the key issue to be addressed by the embedded electronic system.
Recently, it has been proved that the Machine Learning is a promising
approach in processing tactile sensors information. Many studies have
been shown the Machine Learning eectiveness in the classication of input
touch modalities.More specically, this thesis is focused on the stimulation system, allowing
the communication of a mechanical interaction from the electronic skin
to prosthesis users, and the dedicated implementation of algorithms for
processing tactile data originating from the electronic skin. On system
level, the thesis provides design of the experimental setup, experimental
protocol, and of algorithms to process tactile data. On architectural level,
the thesis proposes a design
ow for the implementation of digital circuits
for both FPGA and integrated circuits, and techniques for the power
management of embedded systems for Machine Learning algorithms
Selected Papers from IEEE ICASI 2019
The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology
Accelerated neuromorphic cybernetics
Accelerated mixed-signal neuromorphic hardware refers to electronic systems that emulate electrophysiological aspects of biological nervous systems in analog voltages and currents in an accelerated manner. While the functional spectrum of these systems already includes many observed neuronal capabilities, such as learning or classification, some areas remain largely unexplored. In particular, this concerns cybernetic scenarios in which nervous systems engage in closed interaction with their bodies and environments. Since the control of behavior and movement in animals is both the purpose and the cause of the development of nervous systems, such processes are, however, of essential importance in nature. Besides the design of neuromorphic circuit- and system components, the main focus of this work is therefore the construction and analysis of accelerated neuromorphic agents that are integrated into cybernetic chains of action. These agents are, on the one hand, an accelerated mechanical robot, on the other hand, an accelerated virtual insect. In both cases, the sensory organs and actuators of their artificial bodies are derived from the neurophysiology of the biological prototypes and are reproduced as faithfully as possible. In addition, each of the two biomimetic organisms is subjected to evolutionary optimization, which illustrates the advantages of accelerated neuromorphic nervous systems through significant time savings
Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing
Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial
and technological revolution of the present and future. We envision a world with smart
devices that are able to mimic human behavior (sense, process, and act) and perform
tasks that at one time we thought could only be carried out by humans. The vision
is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware
platforms. However, embedding machine learning algorithms in many application domains
such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing
challenge. A challenge that is controlled by the computational complexity of ML algorithms,
the performance/availability of hardware platforms, and the application\u2019s budget (power
constraint, real-time operation, etc.). In this dissertation, we focus on the design and
implementation of efficient ML algorithms to handle the aforementioned challenges. First, we
apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of
ML algorithms. Then, we design custom Hardware Accelerators to improve the performance
of the implementation within a specified budget. Finally, a tactile data processing application
is adopted for the validation of the proposed exact and approximate embedded machine
learning accelerators.
The dissertation starts with the introduction of the various ML algorithms used for
tactile data processing. These algorithms are assessed in terms of their computational
complexity and the available hardware platforms which could be used for implementation.
Afterward, a survey on the existing approximate computing techniques and hardware
accelerators design methodologies is presented. Based on the findings of the survey, an
approach for applying algorithmic-level ACTs on machine learning algorithms is provided.
Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based
on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow
Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN).
The three accelerators offer a real-time classification with monumental reductions in the
hardware resources and power consumption compared to existing implementations targeting
the same tactile data processing application on FPGA. Moreover, the approximate accelerators
maintain a high classification accuracy with a loss of at most 5%
Run-time reconfiguration for efficient tracking of implanted magnets with a myokinetic control interface applied to robotic hands
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Este trabalho introduz a aplicação de soluções de aprendizagem de máquinas visado ao problema do rastreamento de posição do antebraço baseado em sensores magnéticos. Especi ficamente, emprega-se uma estratégia baseada em dados para criar modelos matemáticos que possam traduzir as informações magnéticas medidas em entradas utilizáveis para dispositivos protéticos. Estes modelos são implementados em FPGAs usando operadores customizados de ponto flutuante para otimizar o consumo de hardware e energia, que são importantes em dispositivos embarcados. A arquitetura de hardware é proposta para ser implementada como um sistema com reconfiguração dinâmica parcial, reduzindo potencialmente a utilização de recursos e o consumo de energia da FPGA. A estratégia de dados proposta e sua implemen tação de hardware pode alcançar uma latência na ordem de microssegundos e baixo consumo de energia, o que encoraja mais pesquisas para melhorar os métodos aqui desenvolvidos para outras aplicações.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).This work introduces the application of embedded machine learning solutions for the problem of magnetic sensors-based limb tracking. Namely, we employ a data-driven strat egy to create mathematical models that can translate the magnetic information measured to usable inputs for prosthetic devices. These models are implemented in FPGAs using cus tomized floating-point operations to optimize hardware and energy consumption, which are important in wearable devices. The hardware architecture is proposed to be implemented as a dynamically partial reconfigured system, potentially reducing resource utilization and power consumption of the FPGA. The proposed data-driven strategy and its hardware implementa tion can achieve a latency in the order of microseconds and low energy consumption, which encourages further research on improving the methods herein devised for other application
Neural networks-on-chip for hybrid bio-electronic systems
PhD ThesisBy modelling the brains computation we can further our understanding
of its function and develop novel treatments for neurological disorders. The
brain is incredibly powerful and energy e cient, but its computation does
not t well with the traditional computer architecture developed over the
previous 70 years. Therefore, there is growing research focus in developing
alternative computing technologies to enhance our neural modelling capability,
with the expectation that the technology in itself will also bene t from
increased awareness of neural computational paradigms.
This thesis focuses upon developing a methodology to study the design
of neural computing systems, with an emphasis on studying systems suitable
for biomedical experiments. The methodology allows for the design to be
optimized according to the application. For example, di erent case studies
highlight how to reduce energy consumption, reduce silicon area, or to
increase network throughput.
High performance processing cores are presented for both Hodgkin-Huxley
and Izhikevich neurons incorporating novel design features. Further, a complete
energy/area model for a neural-network-on-chip is derived, which is
used in two exemplar case-studies: a cortical neural circuit to benchmark
typical system performance, illustrating how a 65,000 neuron network could
be processed in real-time within a 100mW power budget; and a scalable highperformance
processing platform for a cerebellar neural prosthesis. From
these case-studies, the contribution of network granularity towards optimal
neural-network-on-chip performance is explored