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A multiplication reduction technique with near-zero approximation for embedded learning in IoT devices

机译：物联网设备中嵌入式学习的近似零乘法减少技术

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摘要

This paper presents a multiplication reduction technique through near-zero approximation, enabling embedded learning in resource-constrained IoT devices. The intrinsic resilience of neural network and the sparsity of data are identified and utilized. Based on the analysis of leading zero counting and adjustable threshold, intentional approximation is applied to reduce near-zero multiplications. By setting the threshold of the multiplication result to 2-5 and employing ReLU as the neuron activation function, the sparsity of the CNN model can reach 75% with negligible loss in accuracy when recognizing the MNIST data set. Corresponding hardware implementation has been designed and simulated in UMC 65nm process. It can achieve more than 70% improvement of energy efficiency with only 0.37% area overhead of a 256 Multiply-Accumulator array.
机译：本文提出了一种通过接近零的逼近的乘法减少技术，可在资源受限的IoT设备中实现嵌入式学习。识别并利用了神经网络的固有弹性和数据稀疏性。在对前导零计数和可调整阈值进行分析的基础上，采用有意逼近来减少接近零的乘法。通过将乘积结果的阈值设置为2-5，并使用ReLU作为神经元激活函数，当识别MNIST数据集时，CNN模型的稀疏度可以达到75％，而准确性损失可忽略不计。相应的硬件实现已在UMC 65nm工艺中进行了设计和仿真。 256个乘法累加器阵列的面积开销仅为0.37％，可实现70％以上的能源效率提高。

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