Vectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE
2021; Springer Science+Business Media; Volume: 33; Issue: 16 Linguagem: Inglês
10.1007/s00521-021-05814-0
ISSN1433-3058
AutoresMarco Cococcioni, Federico Rossi, Emanuele Ruffaldi, Sergio Saponara,
Tópico(s)Digital Filter Design and Implementation
ResumoAbstract With the arrival of the open-source RISC-V processor architecture, there is the chance to rethink Deep Neural Networks (DNNs) and information representation and processing. In this work, we will exploit the following ideas: i) reduce the number of bits needed to represent the weights of the DNNs using our recent findings and implementation of the posit number system, ii) exploit RISC-V vectorization as much as possible to speed up the format encoding/decoding, the evaluation of activations functions (using only arithmetic and logic operations, exploiting approximated formulas) and the computation of core DNNs matrix-vector operations. The comparison with the well-established architecture ARM Scalable Vector Extension is natural and challenging due to its closedness and mature nature. The results show how it is possible to vectorize posit operations on RISC-V, gaining a substantial speed-up on all the operations involved. Furthermore, the experimental outcomes highlight how the new architecture can catch up, in terms of performance, with the more mature ARM architecture. Towards this end, the present study is important because it anticipates the results that we expect to achieve when we will have an open RISC-V hardware co-processor capable to operate natively with posits.
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