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

机译：具有接近零近似的乘法减少技术，用于IOT设备中的嵌入式学习

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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：本文通过接近零近似提出了乘法还原技术，使嵌入式学习能够在资源受限的物联网中。鉴定并利用了神经网络的内在恢复和数据的稀疏性。基于前导零计数和可调节阈值的分析，应用故意近似以减少零零乘法。通过将乘法结果的阈值设定为2-5并使用Relu作为神经元激活功能，CNN模型的稀疏性可以达到75％，在识别MNIST数据集时的准确性可以忽略不计。在UMC 65NM过程中设计和模拟了相应的硬件实现。它可以在256个乘法堆叠阵列的0.37％的面积上实现超过70％的能效提高。

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