Dec 13, 2017 · Abstract:This work explores the lesser studied objective of optimizing the multiply-and-accumulates executed during evaluation of the ...
Dec 13, 2017 · In particular, we propose using the. Residue Number System (RNS) as the internal num- ber representation across all layer evaluations, allowing.
This work explores the lesser studied objective of optimizing the multiply-and-accumulates executed during evaluation of the network.
This work proposes using the Residue Number System (RNS) as the internal number representation across all layer evaluations, allowing us to explore usage of ...
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Aug 21, 2020 · Reducing the Cost of Neural Network Inference with Residue Number Systems ... Maintaining prediction accuracy using a Residue Number System (RNS).
Example: Given the moduli set {3,4,5}. Compute the conventional number of the residue {2,3,1} with respect to the moduli-set using the Mixed Radix conversion.
[PDF] RNSnet: In-Memory Neural Network Acceleration Using Residue ...
acsweb.ucsd.edu › icrc18_rnsnet
In this work, we propose RNSnet, which uses Residue Number System (RNS) to execute neural network completely in the digital domain in memory. RNSnet simplifies.
Sep 24, 2020 · The goal of this paper is to reduce the resources required to build and operate CNNs. To achieve this goal, a CNN architecture based on Residue ...
First, we show how to extend a residue number system to a self-consistent, non-negative hexagonal coordinate system. Second, we provide a new algorithm for ...
Our study introduces DNNARA-E, an integrated opto-electronic accelerator for machine learning inference using the residue number system. It utilizes one-hot ...