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

Mengenal pasti tahap pengetahuan pelajar tahun akhir Ijazah Sarjana Muda Kejuruteraan di KUiTTHO dalam bidang keusahawanan dari aspek pengurusan modal

Malaysia ialah sebuah negara membangun di dunia. Dalam proses pembangunan ini, hasrat negara untuk melahirkan bakal usahawan beijaya tidak boleh dipandang ringan. Oleh itu, pengetahuan dalam bidang keusahawanan perlu diberi perhatian dengan sewajarnya; antara aspek utama dalam keusahawanan ialah modal. Pengurusan modal yang tidak cekap menjadi punca utama kegagalan usahawan. Menyedari hakikat ini, kajian berkaitan Pengurusan Modal dijalankan ke atas 100 orang pelajar Tahun Akhir Kejuruteraan di KUiTTHO. Sampel ini dipilih kerana pelajar-pelajar ini akan menempuhi alam pekeijaan di mana mereka boleh memilih keusahawanan sebagai satu keijaya. Walau pun mereka bukanlah pelajar dari jurusan perniagaan, namun mereka mempunyai kemahiran dalam mereka cipta produk yang boleh dikomersialkan. Hasil dapatan kajian membuktikan bahawa pelajar-pelajar ini berminat dalam bidang keusahawanan namun masih kurang pengetahuan tentang pengurusan modal terutamanya dalam menentukan modal permulaan, pengurusan modal keija dan caracara menentukan pembiayaan kewangan menggunakan kaedah jualan harian. Oleh itu, satu garis panduan Pengurusan Modal dibina untuk memberi pendedahan kepada mereka

Enhancing a Neurosurgical Imaging System with a PC-based Video Processing Solution

This work presents a PC-based prototype video processing application developed to be used with a specific neurosurgical imaging device, the OPMI® PenteroTM operating microscope, in the Department of Neurosurgery of Helsinki University Central Hospital at Töölö, Helsinki. The motivation for implementing the software was the lack of some clinically important features in the imaging system provided by the microscope. The imaging system is used as an online diagnostic aid during surgery. The microscope has two internal video cameras; one for regular white light imaging and one for near-infrared fluorescence imaging, used for indocyanine green videoangiography. The footage of the microscope’s current imaging mode is accessed via the composite auxiliary output of the device. The microscope also has an external high resolution white light video camera, accessed via a composite output of a separate video hub. The PC was chosen as the video processing platform for its unparalleled combination of prototyping and high-throughput video processing capabilities. A thorough analysis of the platform and efficient video processing methods was conducted in the thesis and the results were used in the design of the imaging station. The features found feasible during the project were incorporated into a video processing application running on a GNU/Linux distribution Ubuntu. The clinical usefulness of the implemented features was ensured beforehand by consulting the neurosurgeons using the original system. The most significant shortcomings of the original imaging system were mended in this work. The key features of the developed application include: live streaming, simultaneous streaming and recording, and playing back of upto two video streams. The playback mode provides full media player controls, with a frame-by-frame precision rewinding, in an intuitive and responsive interface. A single view and a side-by-side comparison mode are provided for the streams. The former gives more detail, while the latter can be used, for example, for before-after and anatomic-angiographic comparisons.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Improving Energy Efficiency of Application-Specific Instruction-Set Processors

Present-day consumer mobile devices seem to challenge the concept of embedded computing by bringing the equivalent of supercomputing power from two decades ago into hand-held devices. This challenge, however, is well met by pushing the boundaries of embedded computing further into areas previously monopolised by Application-Specific Integrated Circuits (ASICs). Furthermore, in areas traditionally associated with embedded computing, an increase in the complexity of algorithms and applications requires a continuous rise in availability of computing power and energy efficiency in order to fit within the same, or smaller, power budget. It is, ultimately, the amount of energy the application execution consumes that dictates the usefulness of a programmable embedded system, in comparison with implementation of an ASIC.This Thesis aimed to explore the energy efficiency overheads of Application-Specific InstructionSet Processors (ASIPs), a class of embedded processors aiming to compete with ASICs. While an ASIC can be designed to provide precise performance and energy efficiency required by a specific application without unnecessary overheads, the cost of design and verification, as well as the inability to upgrade or modify, favour more flexible programmable solutions. The ASIP designs can match the computing performance of the ASIC for specific applications. What is left, therefore, is achieving energy efficiency of a similar order of magnitude.In the past, one area of ASIP design that has been identified as a major consumer of energy is storage of temporal values produced during computation – the Register File (RF), with the associated interconnection network to transport those values between registers and computational Function Units (FUs). In this Thesis, the energy efficiency of RF and interconnection network is studied using the Transport Triggered Architectures (TTAs) template. Specifically, compiler optimisations aiming at reducing the traffic of temporal values between RF and FUs are presented in this Thesis. Bypassing of the temporal value, from the output of the FU which produces it directly in the input ports of the FUs that require it to continue with the computation, saves multiple RF reads. In addition, if all the uses of such a temporal value can be bypassed, the RF write can be eliminated as well. Such optimisations result in a simplification of the RF, via a reduction in the actual number of registers present or a reduction in the number of read and write ports in the RF and improved energy efficiency. In cases where the limited number of the simultaneous RF reads or writes cause a performance bottleneck, such optimisations result in performance improvements leading to faster execution times, therefore, allowing for execution at lower clock frequencies resulting in additional energy savings.Another area of the ASIP design consuming a significant amount of energy is the instruction memory subsystem, which is the artefact required for the programmability of the embedded processor. As this subsystem is not present in ASIC, the energy consumed for storing an application program and reading it from the instruction memories to control processor execution is an overhead that needs to be minimised. In this Thesis, one particular tool to improve the energy efficiency of the instruction memory subsystem – instruction buffer – is examined. While not trivially obvious, the presence of buffers for storing loop bodies, or parts of them, results in a reduced number of reads from the instruction memories. As a result, memories can be put to lower power state leading to lower overall energy consumption, pending energy-efficient buffer implementation. Specifically, an energy-efficient implementation of the instruction buffer is presented in this Thesis, together with analysis tools to identify candidate loops and assess their suitability for storing in the instruction buffer.The studies presented in this Thesis show that the energy overheads associated with the use of embedded processors, in comparison to ad-hoc ASIC solutions, are manageable when carefully considered during the design of an embedded system for a particular application, or application domain. Finally, the methods presented in this Thesis do not restrict the reprogrammability of the embedded system

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

Architectures for Adaptive Low-Power Embedded Multimedia Systems

This Ph.D. thesis describes novel hardware/software architectures for adaptive low-power embedded multimedia systems. Novel techniques for run-time adaptive energy management are proposed, such that both HW & SW adapt together to react to the unpredictable scenarios. A complete power-aware H.264 video encoder was developed. Comparison with state-of-the-art demonstrates significant energy savings while meeting the performance constraint and keeping the video quality degradation unnoticeable

Artificial neural networks acceleration on field-programmable gate arrays considering model redundancy

Artificial Neural Networks (ANNs) have dramatically developed over the last ten years, and have been successfully applied in many important areas. A natural follow-up topic is to deploy ANNs to a wider range of hardware platforms. However, modern ANN models may aim for millisecond- or even nanosecond-level latency for each input processing while it is common for them to require million-level operations and gigabyte-scale data access for computing each input. This intrinsic high computational complexity introduces hardware challenges to the system implementation. Meanwhile, the integration of computing resources on hardware platforms is hampered by the slowing down of Moore’s Law. Therefore, it is important to study new design methods for ANN hardware systems that produce high model accuracy with low resource usage. Field-Programmable Gate Array (FPGA) is a natural fit for this topic due to its reconfigurability and flexibility. These features of FPGA allow us to implement customised data paths and data representations on hardware, which makes it the primary platform in this research. The main topics discussed in this thesis include neural network redundancy and its impact on hardware systems. The main goal is to reduce hardware complexity by reducing neural network redundancy and maintaining accuracy at the same time. To achieve this, redundancy is firstly categorised into two types: model- and data-level. Then, each type is studied in isolation before both are combined in a single system design. First, to study model-level redundancy, an algorithm called dropout is implemented as a way to reduce model-level redundancy during training and used here to reduce hardware cost. Our proposed system achieves a 50% reduction in DSP usage and 33% to 47% fewer on-chip memory usage compared to conventional implementations. Second, in terms of data-level redundancy, we aim to study how data precision affects hardware cost and system throughput. Our experiments show that reduced-precision data present negligible or even no accuracy loss to full-precision data on the tested benchmarks. In particular, the 4-bit fixed point presents a good trade-off between model accuracy and hardware cost compared to other tested data representations. Third, we studied the interactive effect of reducing both model- and data-level redundancy and proposed a FPGA accelerator design for Redundancy-Reduced (RR-) MobileNet [Hea17]. Our proposed RR-MobileNet system achieves a state-of-the-art latency, 7.85 ms, for single image processing in ImageNet inference. Finally, a design guideline is proposed as a step-by-step guidance for redundancy-reduced neural network system design.Open Acces