Linear computation coding - Müller, Ralf; Gäde, Bernhard; Bereyhi, Ali (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2021)
We introduce the new concept of computation coding. For linear functions, we present an algorithm to reduce the computational cost of multiplying an arbitrary given matrix with an unknown vector. It decomposes the given matrix into the product of codebook and wiring matrices whose entries are either zero or signed integer powers of two. For a typical implementation of deep neural networks, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed.
CRIS-Team Scopus Importer:2021-10-29
Keywords: Approximate computing; Fixed-point arithmetic; Neural networks; Quantization