Multi-processor system design with ESPAM

ABSTRAC

Parameterized Looped Schedules

This paper is concerned with the compact representation of execution sequences in terms of efficient looping constructs. Here, by a looping construct we mean a compact way of specifying a finite repetition of a set of execution primitives (“instructions”). Such compaction, which can be viewed as a form of hierarchical run-length encoding, has application in many embedded software contexts, including efficient control generation for Kahn processes, and software synthesis for static dataflow models of computation, such as synchronous dataflow and cyclo-static dataflow. In this paper, we significantly generalize previous models for loop-based code compaction of DSP programs to yield a configurable code compression methodology that exhibits a broad range of achievable trade-offs. Specifically, we formally develop and apply to DSP hardware and software implementation a parameterizable loop scheduling approach with compact format, dynamic reconfigurability, and low overhead decompression

A parallel processor for fast execution of time-adaptive Jacobi algorithms

In this paper we take the class of Jacobi-type algorithms and present a systematic way to derive an architecture for execution of the time adaptive QR and QR \Gamma1 algorithms, two members of the class. We know that Jacobi-type algorithms find natural expression in Cordic arithmetic and that high-throughput implementations ask for parallel operating pipelined Cordic processor elements. Based on this knowledge, we perform algorithmic transformations, exploiting class specific properties, to reduce critical paths, increase throughput and improve structure utilization. The techniques illustrated in the paper are currently used to derive the specifications of a `class optimal' processor into which several Jacobi-type algorithms execute simultaneously. Keywords--- parallel processors, Jacobi-type algorithms, algorithmic transformations, pipelined processors, Cordic-arithmetic. I. Introduction Matrix computations are increasingly finding application in real-time signal processing. A num..