Stochastic computing (SC) has become a promising approximate computing solution by its negligible resource occupancy and ultralow energy consumption. As a potential replacement of accurate multiplication, SC can dramatically mitigate the problematic power consumption by DNNs. However, current SC-multipliers illustrate an extremely imbalanced accuracy across product space, i.e., neglectable noise with large products but significant noise for small ones, which is discordant to the distribution of products by the sparse matrix in neural computing. In this article, we present a heterogeneous SC-multiplier that heuristically performs three divergent approximating multiplication, including “set-to-0,” “look-up-table,” and “low-discrepancy-SC,” for appropriate precision-provision in the whole space of products. Due to those popular DNN models cannot achieve consensus on the boundaries of above operations, a training-involved method is proposed to determine the settings with limited overhead. In this way, those models successively learn the SC-operation characters and exhibit a definitely improvement on network precision. The experiment shows that, for single multiplication, the product noise can be restrained by 36.86% on average, and for multiplication in multiple network models, the accuracy improvement reaches to 5.5% on average. Furthermore, a group of proposed logic-reduction techniques can improve the energy efficiency by 65% in the system-level evaluation.

中文翻译：

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一种噪声驱动的异构随机计算乘法器，用于提高节能 DNN 中的启发式精度

随机计算（SC）以其可忽略的资源占用和超低的能耗已成为一种有前途的近似计算解决方案。作为精确乘法的潜在替代品，SC 可以显着减轻 DNN 的功耗问题。然而，当前的 SC 乘法器在乘积空间中表现出极度不平衡的精度，即大乘积的噪声可忽略而小乘积的噪声很大，这与神经计算中稀疏矩阵的乘积分布不一致。在本文中，我们提出了一种异构 SC 乘法器，它启发式地执行三种不同的近似乘法，包括“设置为 0”、“查找表”和“低差异 SC”，以提供适当的精度在整个产品空间。由于那些流行的 DNN 模型无法就上述操作的边界达成共识，因此提出了一种涉及训练的方法来确定具有有限开销的设置。通过这种方式，这些模型依次学习了 SC 操作特征，并在网络精度上表现出明显的提高。实验表明，对于单次相乘，乘积噪声平均可以抑制36.86%，对于多网络模型相乘，精度平均提高5.5%。此外，一组提出的逻辑减少技术可以在系统级评估中将能源效率提高 65%。这些模型连续学习了 SC 操作特征，并在网络精度上表现出明显的提高。实验表明，对于单次相乘，乘积噪声平均可以抑制36.86%，对于多网络模型相乘，精度平均提高5.5%。此外，一组提出的逻辑减少技术可以在系统级评估中将能源效率提高 65%。这些模型连续学习了 SC 操作特征，并在网络精度上表现出明显的提高。实验表明，对于单次相乘，乘积噪声平均可以抑制36.86%，对于多网络模型相乘，精度平均提高5.5%。此外，一组提出的逻辑减少技术可以在系统级评估中将能源效率提高 65%。