Low Error-Rate Approximate Multiplier Design for DNNs with Hardware-Driven Co-Optimization
In this paper, two approximate 3*3 multipliers are proposed and the synthesis results of the ASAP-7nm process library justify that they can reduce the area by 31.38% and 36.17%, and the power consumption by 36.73% and 35.66% compared with the exact multiplier, respectively. They can be aggregated with a 2*2 multiplier to produce an 8*8 multiplier with low error rate based on the distribution of DNN weights. We propose a hardware-driven software co-optimization method to improve the DNN accuracy by retraining. Based on the proposed two approximate 3-bit multipliers, three approximate 8-bit multipliers with low error-rate are designed for DNNs. Compared with the exact 8-bit unsigned multiplier, our design can achieve a significant advantage over other approximate multipliers on the public dataset.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions. Our multiplier achieves up to 50.24% higher accuracy than t…
QuantizationVocal Bursts Intensity PredictionPositive/Negative Approximate Multipliers for DNN Accelerators
Recent Deep Neural Networks (DNNs) managed to deliver superhuman accuracy levels on many AI tasks. Several applications rely more and more on DNNs to deliver sophisticated services and DNN accelerators are becoming integ…
Leveraging Highly Approximated Multipliers in DNN Inference
In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and s…
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to recover from the accuracy loss caused by the…
Multiobjective OptimizationIs Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
Approximate computing is known for its effectiveness in improvising the energy efficiency of deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recently, the inexact nature of approximate co…
Adversarial Robustness