Low-power Programmable Processor for Fast Fourier Transform Based on Transport Triggered Architecture

This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage. The processor supports all power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can compute 20916 transforms of size 1024.Comment: 5 pages, 4 figures, 1 table, ICASSP 2019 conferenc

Towards adaptive balanced computing (ABC) using reconfigurable functional caches (RFCs)

The general-purpose computing processor performs a wide range of functions. Although the performance of general-purpose processors has been steadily increasing, certain software technologies like multimedia and digital signal processing applications demand ever more computing power. Reconfigurable computing has emerged to combine the versatility of general-purpose processors with the customization ability of ASICs. The basic premise of reconfigurability is to provide better performance and higher computing density than fixed configuration processors. Most of the research in reconfigurable computing is dedicated to on-chip functional logic. If computing resources are adaptable to the computing requirement, the maximum performance can be achieved. To overcome the gap between processor and memory technology, the size of on-chip cache memory has been consistently increasing. The larger cache memory capacity, though beneficial in general, does not guarantee a higher performance for all the applications as they may not utilize all of the cache efficiently. To utilize on-chip resources effectively and to accelerate the performance of multimedia applications specifically, we propose a new architecture---Adaptive Balanced Computing (ABC). ABC uses dynamic resource configuration of on-chip cache memory by integrating Reconfigurable Functional Caches (RFC). RFC can work as a conventional cache or as a specialized computing unit when necessary. In order to convert a cache memory to a computing unit, we include additional logic to embed multi-bit output LUTs into the cache structure. We add the reconfigurability of cache memory to a conventional processor with minimal modification to the load/store microarchitecture and with minimal compiler assistance. ABC architecture utilizes resources more efficiently by reconfiguring the cache memory to computing units dynamically. The area penalty for this reconfiguration is about 50--60% of the memory cell cache array-only area with faster cache access time. In a base array cache (parallel decoding caches), the area penalty is 10--20% of the data array with 1--2% increase in the cache access time. However, we save 27% for FIR and 44% for DCT/IDCT in area with respect to memory cell array cache and about 80% for both applications with respect to base array cache if we were to implement all these units separately (such as ASICs). The simulations with multimedia and DSP applications (DCT/IDCT and FIR/IIR) show that the resource configuration with the RFC speedups ranging from 1.04X to 3.94X in overall applications and from 2.61X to 27.4X in the core computations. The simulations with various parameters indicate that the impact of reconfiguration can be minimized if an appropriate cache organization is selected

High Performance Reconfigurable Computing for Linear Algebra: Design and Performance Analysis

Field Programmable Gate Arrays (FPGAs) enable powerful performance acceleration for scientific computations because of their intrinsic parallelism, pipeline ability, and flexible architecture. This dissertation explores the computational power of FPGAs for an important scientific application: linear algebra. First of all, optimized linear algebra subroutines are presented based on enhancements to both algorithms and hardware architectures. Compared to microprocessors, these routines achieve significant speedup. Second, computing with mixed-precision data on FPGAs is proposed for higher performance. Experimental analysis shows that mixed-precision algorithms on FPGAs can achieve the high performance of using lower-precision data while keeping higher-precision accuracy for finding solutions of linear equations. Third, an execution time model is built for reconfigurable computers (RC), which plays an important role in performance analysis and optimal resource utilization of FPGAs. The accuracy and efficiency of parallel computing performance models often depend on mean maximum computations. Despite significant prior work, there have been no sufficient mathematical tools for this important calculation. This work presents an Effective Mean Maximum Approximation method, which is more general, accurate, and efficient than previous methods. Together, these research results help address how to make linear algebra applications perform better on high performance reconfigurable computing architectures