Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Micro-threading and FPGA implementation of a RISC microprocessor : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

Appendix E removed due to copyright restrictions. Articles are available in the print copy held in the libraryThis thesis is the outcome of research in two areas of computer technology: microprocessor and multi-processor architectures (specifically from the perspective of how differently they tolerate highly-latent and non-deterministic events), and hardware design of complex digital systems containing both datapath and control (particularly microprocessors). This thesis starts by pointing out that in order to achieve high processing speeds, current popular superscalar microprocessors (e.g. Intel Pentiums, Digital Alpha, etc) rely heavily on the technique of speculating the outcome of instruction flow in order to predict the behaviour of non-deterministic computing operations (as in loading operands from high-latency memory into the processor). This is fine only if the speculation is correct. But, what if it isn't? If the speculation fails, this would mean that the processor has to abandon its current decision (which now proved to be the wrong one) for the instruction flow path taken and to start all over again with the other path (the actual correct one). This is a waste of valuable processing time and hardware resources and a reduction of performance when speculation fails. Therefore, these processors can achieve high performance only when the majority of speculations are successful (being able to predict the right path). In an attempt to overcome the above shortcomings, the first part of this thesis is an investigation of the novel vector micro-threading architecture as an alternative approach to the current superscalar-based speculative microprocessor designs. Micro-threading is based on the not-so-novel multithreading technique, which avoids speculation altogether and instead, starts running a different thread of instructions while waiting for the non-determinism to be resolved. This utilizes the chip resources more efficiently without waste of any processing power. The rest of this thesis focuses on the baseline RISC processor platform, the MIPS R2000, which is reviewed first then partially synthesized from the RTL (Register Transfer Level) description using VHDL and then simulated and tested. This is conducted in order for future research to build upon and add the micro-threading architectural add-ons and modifications. Keywords: Micro-threading, Latency Tolerance, FPGA Synthesis, RISC Architecture, MIPS R2000 processor, VHDL