1,793 research outputs found
Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems
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.
Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks
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