Hardware Acceleration Using Functional Languages

Cílem této práce je prozkoumat možnosti využití funkcionálního paradigmatu pro hardwarovou akceleraci, konkrétně pro datově paralelní úlohy. Úroveň abstrakce tradičních jazyků pro popis hardwaru, jako VHDL a Verilog, přestáví stačit. Pro popis na algoritmické či behaviorální úrovni se rozmáhají jazyky původně navržené pro vývoj softwaru a modelování, jako C/C++, SystemC nebo MATLAB. Funkcionální jazyky se s těmi imperativními nemůžou měřit v rozšířenosti a oblíbenosti mezi programátory, přesto je předčí v mnoha vlastnostech, např. ve verifikovatelnosti, schopnosti zachytit inherentní paralelismus a v kompaktnosti kódu. Pro akceleraci datově paralelních výpočtů se často používají jednotky FPGA, grafické karty (GPU) a vícejádrové procesory. Praktická část této práce rozšiřuje existující knihovnu Accelerate pro počítání na grafických kartách o výstup do VHDL. Accelerate je možno chápat jako doménově specifický jazyk vestavěný do Haskellu s backendem pro prostředí NVIDIA CUDA. Rozšíření pro vysokoúrovňovou syntézu obvodů ve VHDL představené v této práci používá stejný jazyk a frontend.The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.

Automated generation of custom processor core from C code

We present a method for construction of application-specific processor cores from a given C code. Our approach consists of three phases. We start by quantifying the properties of the C code in terms of operation types, available parallelism, and other metrics. We then create an initial data path to exploit the available parallelism. We then apply designer-guided constraints to an interactive data path refinement algorithm that attempts to reduce the number of the most expensive components while meeting the constraints. Our experimental results show that our technique scales very well with the size of the C code. We demonstrate the efficiency of our technique on wide range of applications, from standard academic benchmarks to industrial size examples like the MP3 decoder. Each processor core was constructed and refined in under a minute, allowing the designer to explore several different configurations in much less time than needed for manual design. We compared our selection algorithm to the manual selection in terms of cost/performance and showed that our optimization technique achieves better cost/performance trade-off. We also synthesized our designs with programmable controller and, on average, the refined core have only 23% latency overhead, twice as many block RAMs and 36% fewer slices compared to the respective manual designs

Design of asynchronous microprocessor for power proportionality

PhD ThesisMicroprocessors continue to get exponentially cheaper for end users following Moore’s law, while the costs involved in their design keep growing, also at an exponential rate. The reason is the ever increasing complexity of processors, which modern EDA tools struggle to keep up with. This makes further scaling for performance subject to a high risk in the reliability of the system. To keep this risk low, yet improve the performance, CPU designers try to optimise various parts of the processor. Instruction Set Architecture (ISA) is a significant part of the whole processor design flow, whose optimal design for a particular combination of available hardware resources and software requirements is crucial for building processors with high performance and efficient energy utilisation. This is a challenging task involving a lot of heuristics and high-level design decisions. Another issue impacting CPU reliability is continuous scaling for power consumption. For the last decades CPU designers have been mainly focused on improving performance, but “keeping energy and power consumption in mind”. The consequence of this was a development of energy-efficient systems, where energy was considered as a resource whose consumption should be optimised. As CMOS technology was progressing, with feature size decreasing and power delivered to circuit components becoming less stable, the energy resource turned from an optimisation criterion into a constraint, sometimes a critical one. At this point power proportionality becomes one of the most important aspects in system design. Developing methods and techniques which will address the problem of designing a power-proportional microprocessor, capable to adapt to varying operating conditions (such as low or even unstable voltage levels) and application requirements in the runtime, is one of today’s grand challenges. In this thesis this challenge is addressed by proposing a new design flow for the development of an ISA for microprocessors, which can be altered to suit a particular hardware platform or a specific operating mode. This flow uses an expressive and powerful formalism for the specification of processor instruction sets called the Conditional Partial Order Graph (CPOG). The CPOG model captures large sets of behavioural scenarios for a microarchitectural level in a computationally efficient form amenable to formal transformations for synthesis, verification and automated derivation of asynchronous hardware for the CPU microcontrol. The feasibility of the methodology, novel design flow and a number of optimisation techniques was proven in a full size asynchronous Intel 8051 microprocessor and its demonstrator silicon. The chip showed the ability to work in a wide range of operating voltage and environmental conditions. Depending on application requirements and power budget our ASIC supports several operating modes: one optimised for energy consumption and the other one for performance. This was achieved by extending a traditional datapath structure with an auxiliary control layer for adaptable and fault tolerant operation. These and other optimisations resulted in a reconfigurable and adaptable implementation, which was proven by measurements, analysis and evaluation of the chip.EPSR

Datapath and memory co-optimization for FPGA-based computation

With the large resource densities available on modern FPGAs it is often the available memory bandwidth that limits the parallelism (and therefore performance) that can be achieved. For this reason the focus of this thesis is the development of an integrated scheduling and memory optimisation methodology to allow high levels of parallelism to be exploited in FPGA based designs. A manual translation from C to hardware is first investigated as a case study, exposing a number of potential optimisation techniques that have not been exploited in existing work. An existing outer loop pipelining approach, originally developed for VLIW processors, is extended and adapted for application to FPGAs. The outer loop pipelining methodology is first developed to use a fixed memory subsystem design and then extended to automate the optimisation of the memory subsystem. This approach allocates arrays to physical memories and selects the set of data reuse structures to implement to match the available and required memory bandwidths as the pipelining search progresses. The final extension to this work is to include the partitioning of data from a single array across multiple physical memories, increasing the number of memory ports through which data my be accessed. The facility for loop unrolling is also added to increase the potential for parallelism and exploit the additional bandwidth that partitioning can provide. We describe our approach based on formal methodologies and present the results achieved when these methods are applied to a number of benchmarks. These results show the advantages of both extending pipelining to levels above the innermost loop and the co-optimisation of the datapath and memory subsystem