General matrix multiplication (GEMM) is pervasive in various domains, such as signal processing, computer vision, and machine learning. Conventional binary architectures for GEMM exhibit poor scalability in area and energy efficiency, due to the spatial nature of number representation and computing. On the contrary, unary computing processes data in temporal domain with extremely simple logic. However, to date, there rarely exist efficient architectures for unary GEMM. In this work, we first present uGEMM, a hardware-efficient unary GEMM architecture enabled by universally compatible arithmetic units, which simultaneously achieves input-insensitivity and high output accuracy. Next, we demonstrate that the proposed uGEMM can reliably early terminate the computation and offers dynamic energy-accuracy scaling for real-world applications via an accuracy-aware metric. Finally, to propel the future research for unary computing, we open source our unary computing simulator, UnarySim.

中文翻译：

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uGEMM：GEMM 应用的一元计算

通用矩阵乘法 (GEMM) 在各个领域都很普遍，例如信号处理、计算机视觉和机器学习。由于数字表示和计算的空间性质，GEMM 的传统二进制架构在面积和能源效率方面表现出较差的可扩展性。相反，一元计算以极其简单的逻辑处理时域中的数据。然而，迄今为止，几乎没有一元 GEMM 的有效架构。在这项工作中，我们首先提出了 uGEMM，这是一种硬件高效的一元 GEMM 架构，由通用兼容的算术单元启用，同时实现了输入不敏感和高输出精度。下一个，我们证明了所提出的 uGEMM 可以可靠地提前终止计算，并通过准确度感知指标为现实世界的应用程序提供动态能量准确度缩放。最后，为了推动一元计算的未来研究，我们开源了一元计算模拟器UnarySim。