A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

Kernekoncepter

A&B BNN introduces an innovative approach to eliminate multiplication operations in binary neural networks, achieving competitive performance with state-of-the-art models.

Resumé

Abstract:

Binary neural networks reduce storage and computational demands.
A&B BNN eliminates multiplication operations, introducing mask layer and quantized RPReLU structure.

Introduction:

Neural networks advancements face computational challenges.
Hardware-efficient architectures like SNNs eliminate multiplication operations.

Binary Network Architecture:

A&B BNN eliminates all multiplication operations during inference.
Experimental results show competitive performance on CIFAR-10, CIFAR-100, and ImageNet datasets.

Method:

Scaled weight standardization and adaptive gradient clipping techniques are employed.
Distillation loss functions enforce similarity between full-precision and binary networks.

Hardware Benefits:

A&B BNN reduces hardware overhead significantly.
Enables inference entirely within the chip, improving real-time performance.

Experiments:

Achieved competitive accuracies on CIFAR-10, CIFAR-100, and ImageNet datasets.
Ablation studies show the effectiveness of quantized RPReLU and optimal hyperparameters.

Visualization:

Quantized RPReLU enhances network nonlinearity and performance.
Distribution of quantization slopes demonstrates improved expression in ReActNet-18 and ReActNet-A.

Statistik

Binary neural networks utilize 1-bit quantized weights and activations.
Experimental results achieved 92.30%, 69.35%, and 66.89% on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively.

Citater

"A&B BNN eliminates all multiplication operations during inference."
"Experimental results achieved competitive performance compared to the state-of-the-art."

Vigtigste indsigter udtrukket fra

A&B BNN

by Ruichen Ma,G... kl. arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03739.pdf

Dybere Forespørgsler

질문 1

이항 신경망의 전체 효율성에 대한 곱셈 연산 제거의 영향은 무엇인가요?
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질문 2

제안된 A&B BNN 접근 방식의 잠재적인 단점이나 제한 사항은 무엇인가요?
답변 2 여기에

질문 3

이 연구 결과를 바이너리 네트워크 이외의 신경망 연구 분야에 어떻게 적용할 수 있을까요?
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