A Genetic-algorithm-based Approach to the Design of DCT Hardware Accelerators

As modern applications demand an unprecedented level of computational resources, traditional computing system design paradigms are no longer adequate to guarantee significant performance enhancement at an affordable cost. Approximate Computing (AxC) has been introduced as a potential candidate to achieve better computational performances by relaxing non-critical functional system specifications. In this article, we propose a systematic and high-abstraction-level approach allowing the automatic generation of near Pareto-optimal approximate configurations for a Discrete Cosine Transform (DCT) hardware accelerator. We obtain the approximate variants by using approximate operations, having configurable approximation degree, rather than full-precise ones. We use a genetic searching algorithm to find the appropriate tuning of the approximation degree, leading to optimal tradeoffs between accuracy and gains. Finally, to evaluate the actual HW gains, we synthesize non-dominated approximate DCT variants for two different target technologies, namely, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). Experimental results show that the proposed approach allows performing a meaningful exploration of the design space to find the best tradeoffs in a reasonable time. Indeed, compared to the state-of-the-art work on approximate DCT, the proposed approach allows an 18% average energy improvement while providing at the same time image quality improvement

Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance

High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search

Scheduling and partitioning Vlsi circuit operating at multiple supply voltages

With today\u27s increasingly large and complex digital integrated circuit (IC) and system-on-chip designs, power dissipation has emerged as a primary design consideration. Reduction of power consumption in VLSI designs can be achieved at various levels of the design hierarchy, ranging from processing technology, circuit, logic, architectural and algorithmic (behavioral) levels, up to system level. It has also been long recognized that the most dramatic power saving is achievable at the algorithm and architecture levels, where computations are normally described using data/control flow graph. Thus, in this thesis, a multiple supply voltage IC is synthesized at the behavior level; There are, however, a number of practical problems that must be overcome before use of multiple supply voltage becomes prevalent. In particular, lower power is achieved along with an expensive routing cost. Therefore, unlike the existing methods where only scheduling is considered, our synthesis scheme considers both scheduling and partitioning to reduce power consumption due to the functional units as well as the routing cost; The concerned problem is subsequently referred as the multiple voltage scheduling and partitioning problem (MVSP). The MVSP problem is proved to be NP-complete and three behavioral level synthesis algorithms are proposed to minimize power consumption with resources operating at multiple voltages. One is the polynomial time algorithm. The others are heuristic algorithms, which are tabu search algorithm (TS), and simulated annealing algorithm (SA); In the polynomial time algorithm, synthesis is based on the following three-step process. First, one particular supply voltage (selected from a finite and known number of supply voltage levels) is to be determined for each operation in a data flow graph. Then various operations are scheduled so that the power consumption under given time and/or resource constraints can be minimized. Finally, operations are partitioned into different regions running in different supply voltages to minimize the interconnection costs; In TS and SA algorithms, synthesis schemes are performed to minimize the power consumed by resources and interconnections. In particular, we have configured our solutions with a three-tuple vector to account for both the resource assignment and the partition of operation nodes. Special move operation is designed that allows the scheduling and the partitioning to be performed simultaneously; Experiments with a number of digital signal processing benchmarks show that the proposed algorithms achieve the power reduction at different percentage

Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems

Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research has predominantly focused on ASICs, while not achieving similar gains when deployed for FPGA-based accelerator systems, due to the inherent architectural differences between the two. In this work, we propose a novel framework, Xel-FPGAs, which leverages statistical or machine learning models to effectively explore the architecture-space of state-of-the-art ASIC-based approximate circuits to cater them for FPGA-based systems given a simple RTL description of the target application. We have also evaluated the scalability of our framework on a multi-stage application using a hierarchical search strategy. The Xel-FPGAs framework is capable of reducing the exploration time by up to 95%, when compared to the default synthesis, place, and route approaches, while identifying an improved set of Pareto-optimal designs for a given application, when compared to the state-of-the-art. The complete framework is open-source and available online at https://github.com/ehw-fit/xel-fpgas.Comment: Accepted for publication at the 42nd International Conference on Computer-Aided Design (ICCAD), November 2023, San Francisco, CA, US

Designing parameterizable hardware IPs in a model-based design environment for high-level synthesis

Model-based hardware design allows one to map a single model to multiple hardware and/or software architectures, essentially eliminating one of the major limitations of manual coding in C or RTL. Model-based design for hardware implementation has traditionally offered a limited set of microarchitectures, which are typically suitable only for some application scenarios. In this article we illustrate how digital signal processing (DSP) algorithms can be modeled as flexible intellectual property blocks to be used within the popular Simulink model-based design environment. These blocks are written in C and are designed for both functional simulation and hardware implementation, including architectural design space exploration and hardware implementation through high-level synthesis. A key advantage of our modeling approach is that the very same bit-accurate model is used for simulation and high-level synthesis. To prove the feasibility of our proposed approach, we modeled a fast Fourier transform (FFT) algorithm and synthesized it for different DSP applications with very different performance and cost requirements. We also implemented a high-level-synthesis (HLS) intellectual property (IP) generator that can generate flexible FFT HLS-IP blocks that can be mapped to multiple micro-/macroarchitectures, to enable design space exploration as well as being used for functional simulation in the Simulink environment.</jats:p

Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

Automatic sound synthesizer programming: techniques and applications

The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis: tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a similar sound to the target. The techniques for achieving this that are investigated are genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis space explorer provides a user with a representation of the space of possible sounds that a synthesizer can produce and allows them to interactively explore this space. The applications of automatic sound synthesizer programming that are investigated include studio tools, an autonomous musical agent and a self-reprogramming drum machine. The research employs several methodologies: the development of novel software frameworks and tools, the examination of existing software at the source code and performance levels and user trials of the tools and software. The main contributions made are: a method for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme