Baseband analog front-end and digital back-end for reconfigurable multi-standard terminals

Multimedia applications are driving wireless network operators to add high-speed data services such as Edge (E-GPRS), WCDMA (UMTS) and WLAN (IEEE 802.11a,b,g) to the existing GSM network. This creates the need for multi-mode cellular handsets that support a wide range of communication standards, each with a different RF frequency, signal bandwidth, modulation scheme etc. This in turn generates several design challenges for the analog and digital building blocks of the physical layer. In addition to the above-mentioned protocols, mobile devices often include Bluetooth, GPS, FM-radio and TV services that can work concurrently with data and voice communication. Multi-mode, multi-band, and multi-standard mobile terminals must satisfy all these different requirements. Sharing and/or switching transceiver building blocks in these handsets is mandatory in order to extend battery life and/or reduce cost. Only adaptive circuits that are able to reconfigure themselves within the handover time can meet the design requirements of a single receiver or transmitter covering all the different standards while ensuring seamless inter-interoperability. This paper presents analog and digital base-band circuits that are able to support GSM (with Edge), WCDMA (UMTS), WLAN and Bluetooth using reconfigurable building blocks. The blocks can trade off power consumption for performance on the fly, depending on the standard to be supported and the required QoS (Quality of Service) leve

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Optimizing Transformer Inference on FPGA: A Study on Hardware Acceleration using Vitis HLS

In the last decade, advancements in Natural Language Processing have been exemplary, it has reshaped human-computer interaction, spruced up mainly by the transformative power of deep learning models like the Transformer architecture. With its revolutionary self-attention mechanism, the Transformer has outperformed & outmanoeuvred traditional architectures, enhancing tasks varying from machine translation to sentiment analysis. However, the computational demands of these models challenge their integration onto devices with limited resources. This thesis strives to propose an FPGA-based hardware accelerator tailor made for the Transformer's encoder block, implemented using the Vitis High-Level Synthesis (HLS) framework. In this work, we systematically analyze the Transformer to pinpoint computational bottlenecks with alacrity. Through the Vitis HLS framework, the accelerator emphasizes parallelism, resource efficiency, and optimized memory access. Significantly, this approach employs HLS optimization to boost performance to stellar levels. A key contribution is the seamless integration of this accelerator with the Xilinx ecosystem, enriching its deployment on FPGA devices. We subject the proposed accelerator to rigorous & intensive testing, benchmarking its performance, optimum resource utilization, and energy efficiency. The results underscore the accelerator's potential in bridging the computational gap in resource-limited settings, establishing a benchmark for future NLP hardware acceleration endeavors

Design of application-specific instruction set processors with asynchronous methodology for embedded digital signal processing applications.

Kwok Yan-lun Andy.Thesis submitted in: November 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 133-137).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.iiAcknowledgements --- p.iiiList of Figures --- p.viiList of Tables and Examples --- p.xChapter 1. --- Introduction --- p.1Chapter 1.1. --- Motivation --- p.1Chapter 1.2. --- Objective and Approach --- p.4Chapter 1.3. --- Thesis Organization --- p.5Chapter 2. --- Related Work --- p.7Chapter 2.1. --- Coverage --- p.7Chapter 2.2. --- ASIP Design Methodologies --- p.8Chapter 2.3. --- Asynchronous Technology on Processors --- p.12Chapter 2.4. --- Summary --- p.14Chapter 3. --- Asynchronous Design Methodology --- p.15Chapter 3.1. --- Overview --- p.15Chapter 3.2. --- Asynchronous Design Style --- p.17Chapter 3.2.1. --- Micropipelines --- p.17Chapter 3.2.2. --- Fine-grain Pipelining --- p.20Chapter 3.2.3. --- Globally-Asynchronous Locally-Synchronous (GALS) Design --- p.22Chapter 3.3. --- Advantages of GALS in ASIP Design --- p.27Chapter 3.3.1. --- Reuse of Synchronous and Asynchronous IP --- p.27Chapter 3.3.2. --- Fine Tuning of Performance and Power Consumption --- p.27Chapter 3.3.3. --- Synthesis-based Design Flow --- p.28Chapter 3.4. --- Design of GALS Asynchronous Wrapper --- p.28Chapter 3.4.1. --- Handshake Protocol --- p.28Chapter 3.4.2. --- Pausible Clock Generator --- p.29Chapter 3.4.3. --- Port Controllers --- p.30Chapter 3.4.4. --- Performance of the Asynchronous Wrapper --- p.33Chapter 3.5. --- Summary --- p.35Chapter 4. --- Platform Based ASIP Design Methodology --- p.36Chapter 4.1. --- Platform Based Approach --- p.36Chapter 4.1.1. --- The Definition of Our Platform --- p.37Chapter 4.1.2. --- The Definition of the Platform Based Design --- p.37Chapter 4.2. --- Platform Architecture --- p.38Chapter 4.2.1. --- The Nature of DSP Algorithms --- p.38Chapter 4.2.2. --- Design Space of Datapath Optimization --- p.46Chapter 4.2.3. --- Proposed Architecture --- p.49Chapter 4.2.4. --- The Strategy of Realizing an Optimized Datapath --- p.51Chapter 4.2.5. --- Pipeline Organization --- p.59Chapter 4.2.6. --- GALS Partitioning --- p.61Chapter 4.2.7. --- Operation Mechanism --- p.63Chapter 4.3. --- Overall Design Flow --- p.67Chapter 4.4. --- Summary --- p.70Chapter 5. --- Design of the ASIP Platform --- p.72Chapter 5.1. --- Design Goal --- p.72Chapter 5.2. --- Instruction Fetch --- p.74Chapter 5.2.1. --- Instruction fetch unit --- p.74Chapter 5.2.2. --- Zero-overhead loops and Subroutines --- p.75Chapter 5.3. --- Instruction Decode --- p.77Chapter 5.3.1. --- Instruction decoder --- p.77Chapter 5.3.2. --- The Encoding of Parallel and Complex Instructions --- p.80Chapter 5.4. --- Datapath --- p.81Chapter 5.4.1. --- Base Functional Units --- p.81Chapter 5.4.2. --- Functional Unit Wrapper Interface --- p.83Chapter 5.5. --- Register File Systems --- p.84Chapter 5.5.1. --- Memory Hierarchy --- p.84Chapter 5.5.2. --- Register File Organization --- p.85Chapter 5.5.3. --- Address Generation --- p.93Chapter 5.5.4. --- Load and Store --- p.98Chapter 5.6. --- Design Verification --- p.100Chapter 5.7. --- Summary --- p.104Chapter 6. --- Case Studies --- p.105Chapter 6.1. --- Objective --- p.105Chapter 6.2. --- Approach --- p.105Chapter 6.3. --- Based versus Optimized --- p.106Chapter 6.3.1. --- Matrix Manipulation --- p.106Chapter 6.3.2. --- Autocorrelation --- p.109Chapter 6.3.3. --- CORDIC --- p.110Chapter 6.4. --- Optimized versus Advanced Commercial DSPs --- p.113Chapter 6.4.1. --- Introduction to TMS320C62x and SC140 --- p.113Chapter 6.4.2. --- Results --- p.115Chapter 6.5. --- Summary --- p.116Chapter 7. --- Conclusion --- p.118Chapter 7.1. --- When ASIPs encounter asynchronous --- p.118Chapter 7.2. --- Contributions --- p.120Chapter 7.3. --- Future Directions --- p.121Chapter A --- Synthesis of Extended Burst-Mode Asynchronous Finite State Machine --- p.122Chapter B --- Base Instruction Set --- p.124Chapter C --- Special Registers --- p.127Chapter D --- Synthesizable Model of GALS Wrapper --- p.130Reference --- p.13

Reconfigurable acceleration of Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been successful in a wide range of applications involving temporal sequences such as natural language processing, speech recognition and video analysis. However, RNNs often require a significant amount of memory and computational resources. In addition, the recurrent nature and data dependencies in RNN computations can lead to system stall, resulting in low throughput and high latency. This work describes novel parallel hardware architectures for accelerating RNN inference using Field-Programmable Gate Array (FPGA) technology, which considers the data dependencies and high computational costs of RNNs. The first contribution of this thesis is a latency-hiding architecture that utilizes column-wise matrix-vector multiplication instead of the conventional row-wise operation to eliminate data dependencies and improve the throughput of RNN inference designs. This architecture is further enhanced by a configurable checkerboard tiling strategy which allows large dimensions of weight matrices, while supporting element-based parallelism and vector-based parallelism. The presented reconfigurable RNN designs show significant speedup over CPU, GPU, and other FPGA designs. The second contribution of this thesis is a weight reuse approach for large RNN models with weights stored in off-chip memory, running with a batch size of one. A novel blocking-batching strategy is proposed to optimize the throughput of large RNN designs on FPGAs by reusing the RNN weights. Performance analysis is also introduced to enable FPGA designs to achieve the best trade-off between area, power consumption and performance. Promising power efficiency improvement has been achieved in addition to speeding up over CPU and GPU designs. The third contribution of this thesis is a low latency design for RNNs based on a partially-folded hardware architecture. It also introduces a technique that balances initiation interval of multi-layer RNN inferences to increase hardware efficiency and throughput while reducing latency. The approach is evaluated on a variety of applications, including gravitational wave detection and Bayesian RNN-based ECG anomaly detection. To facilitate the use of this approach, we open source an RNN template which enables the generation of low-latency FPGA designs with efficient resource utilization using high-level synthesis tools.Open Acces

Design and implementation of DA FIR filter for bio-inspired computing architecture

This paper elucidates the system construct of DA-FIR filter optimized for design of distributed arithmetic (DA) finite impulse response (FIR) filter and is based on architecture with tightly coupled co-processor based data processing units. With a series of look-up-table (LUT) accesses in order to emulate multiply and accumulate operations the constructed DA based FIR filter is implemented on FPGA. The very high speed integrated circuit hardware description language (VHDL) is used implement the proposed filter and the design is verified using simulation. This paper discusses two optimization algorithms and resulting optimizations are incorporated into LUT layer and architecture extractions. The proposed method offers an optimized design in the form of offers average miminimizations of the number of LUT, reduction in populated slices and gate minimization for DA-finite impulse response filter. This research paves a direction towards development of bio inspired computing architectures developed without logically intensive operations, obtaining the desired specifications with respect to performance, timing, and reliability

Simulační model synchronního motoru s permanentními magnety pro FPGA aplikace a HIL testování

The concept is to create a Simulink model based on the mathematical model of the Permanent Magnet Synchronous Motor; Frequency convertor is used to control the speed of the motor (0 to max RPM). Mathematical equations are derived. As per the derivations the Simulink block is created. All the blocks are from Simulink libraries. The model is converted into code and loaded into a FPGA, this chip will be tested in a HIL testing machine for various conditions and the simulation results from the SIMULINK and HIL testing are compared.Cílem práce je vytvořit simulační model PMSM v prostředí Simulink, založený na matematickém modelu synchronního motoru s permanentními magnety. Simulovaný frekvenční měnič se používá k řízení otáček motoru (0 až max. RPM). Jsou odvozeny matematické rovnice. Na základě odvozených rovnice je vytvořen model v prostředí Simulink. Všechny bloky jsou ze základních knihoven Simulinku. Model PMSM je poté s využitím speciálních bloků a knihoven převeden na simulační model pro FPGA apliakce. Výsledný model je nahrán do FPGA čipu testovacího zařízení HIL a bude využíván pro testování řídicích jednotek elektrických pohonů. Výsledky simulací z prostředí Simulink a reálné aplikace HIL jsou porovnány.430 - Katedra elektronikydobř