CANET: Quantized Neural Network Inference With 8-bit Carry-Aware Accumulator

CANET: Quantized Neural Network Inference With 8-bit Carry-Aware Accumulator

Neural network quantization represents weights and activations with few bits, greatly reducing the overhead of multiplications. However, due to the recursive accumulation operations, high-precision accumulators are still required in multiply-accumulate (MAC) units to avoid overflow, incurring signif...

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Bibliographic Details
Main Authors: Jingxuan Yang, Xiaoqin Wang, Yiying Jiang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
quantization
efficient inference
low-precision accumulator
Online Access:https://ieeexplore.ieee.org/document/10445180/

Internet

https://ieeexplore.ieee.org/document/10445180/

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