Instruction-set architecture exploration of VLIW ASIPs using a genetic algorithm

Genetic algorithms are commonly used for automatically solving complex design problem because exploration using genetic algorithms can consistently deliver good results when the algorithm is given a long enough run-time. However, the exploration time for problems with huge design spaces can be very long, often making exploration using a genetic algorithm practically infeasible. In this work, we present a genetic algorithm for exploring the instruction-set architecture of VLIW ASIPs and demonstrate its effectiveness by comparing it to two heuristic algorithms. We present several optimizations to the genetic algorithm configuration, and demonstrate how caching of intermediate compilation and simulation results can reduce the exploration time by an order of magnitude

A Micro Power Hardware Fabric for Embedded Computing

Field Programmable Gate Arrays (FPGAs) mitigate many of the problemsencountered with the development of ASICs by offering flexibility, faster time-to-market, and amortized NRE costs, among other benefits. While FPGAs are increasingly being used for complex computational applications such as signal and image processing, networking, and cryptology, they are far from ideal for these tasks due to relatively high power consumption and silicon usage overheads compared to direct ASIC implementation. A reconfigurable device that exhibits ASIC-like power characteristics and FPGA-like costs and tool support is desirable to fill this void. In this research, a parameterized, reconfigurable fabric model named as domain specific fabric (DSF) is developed that exhibits ASIC-like power characteristics for Digital Signal Processing (DSP) style applications. Using this model, the impact of varying different design parameters on power and performance has been studied. Different optimization techniques like local search and simulated annealing are used to determine the appropriate interconnect for a specific set of applications. A design space exploration tool has been developed to automate and generate a tailored architectural instance of the fabric.The fabric has been synthesized on 160 nm cell-based ASIC fabrication process from OKI and 130 nm from IBM. A detailed power-performance analysis has been completed using signal and image processing benchmarks from the MediaBench benchmark suite and elsewhere with comparisons to other hardware and software implementations. The optimized fabric implemented using the 130 nm process yields energy within 3X of a direct ASIC implementation, 330X better than a Virtex-II Pro FPGA and 2016X better than an Intel XScale processor

Real-Time neural signal decoding on heterogeneous MPSocs based on VLIW ASIPs

An important research problem, at the basis of the development of embedded systems for neuroprosthetic applications, is the development of algorithms and platforms able to extract the patient's motion intention by decoding the information encoded in neural signals. At the state of the art, no portable and reliable integrated solutions implementing such a decoding task have been identified. To this aim, in this paper, we investigate the possibility of using the MPSoC paradigm in this application domain. We perform a design space exploration that compares different custom MPSoC embedded architectures, implementing two versions of a on-line neural signal decoding algorithm, respectively targeting decoding of single and multiple acquisition channels. Each considered design points features a different application configuration, with a specific partitioning and mapping of parallel software tasks, executed on customized VLIW ASIP processing cores. Experimental results, obtained by means of FPGA-based prototyping and post-floorplanning power evaluation on a 40nm technology library, assess the performance and hardware-related costs of the considered configurations. The reported power figures demonstrate the usability of the MPSoC paradigm within the processing of bio-electrical signals and show the benefits achievable by the exploitation of the instruction-level parallelism within tasks

Combining on-hardware prototyping and high level simulation for DSE of multi-ASIP system

Abstract-Modern heterogeneous multi-processor embedded systems very often expose to the designer a large number of degrees of freedom, related to the application partitioning/mapping and to the component-and system-level architecture composition. The number is even larger when the designer targets systems based on configurable Application Specific Instructionset Processors, due to the fine customizability of their internal architecture. This poses the need for effective and user-friendly design tools, capable to deal with the extremely wide systemlevel design space exposed by multi-processor architecture and, at the same time, with an extended variety of processing element architectural configurations, to be evaluated in detail and in reasonable times. As a possible solution, within the MADNESS project [1], an integrated toolset has been proposed, combining the benefits of novel fast FPGA-based prototyping techniques with those provided by high-level simulation. In the toolset, the resulting evaluation platform serves as an underlying layer for a Design Space search algorithm. The paper presents the individual tools included in the toolset and their interaction strategy. The approach is then evaluated with a design space exploration case study, taking as a target application a video compression kernel. The integrated toolset has been used to produce a Pareto front of evaluated system-level configurations

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