Gbit/second lossless data compression hardware

This thesis investigates how to improve the performance of lossless data compression hardware as a tool to reduce the cost per bit stored in a computer system or transmitted over a communication network. Lossless data compression allows the exact reconstruction of the original data after decompression. Its deployment in some high-bandwidth applications has been hampered due to performance limitations in the compressing hardware that needs to match the performance of the original system to avoid becoming a bottleneck. Advancing the area of lossless data compression hardware, hence, offers a valid motivation with the potential of doubling the performance of the system that incorporates it with minimum investment. This work starts by presenting an analysis of current compression methods with the objective of identifying the factors that limit performance and also the factors that increase it. [Continues.

A system-on-chip vector multiprocessor for transmission line modelling acceleration

We discuss a configurable, System-on-Chip vector multiprocessor for accelerating the Transmission Line Modeling (TLM) algorithm with an architecture capable of exploiting the two primary forms of parallelism in the code, thread and data level parallelism. Theoretical results demonstrate an order of magnitude reduction in the dynamic instruction count for a scalar-processor/vector-coprocessor configuration at a vector length of sixteen 32-bit singleprecision elements. Furthermore, a multi-vector SoC architecture consisting of ten such vector accelerators provides a near-linear theoretical performance benefit of the order of 88% in three out of four benchmark configurations which is orthogonal to the benefit realized by vectorization alone. We discuss in detail this potent architecture and present implementation data for the 2-way multi-processor VLSI macrocell