Local processing of machine learning algorithms like support vector machine (SVM) is preferred over the cloud for many real-time embedded applications. However, such embedded systems often have stringent energy constraints besides throughput and accuracy requirements. Hence, hardware-efficient design to compute SVM is critical to enable these applications. In this paper, a hardware-efficient SVM learning unit is proposed using reduced number of multiplications and approximate computing techniques. These design techniques helped the learning unit to achieve 46.97% and 35.72% reductions in area and power when compared with those of the design using full multipliers. The proposed SVM learning unit supports on-chip training and classification. Energy-efficient dual-core, quad-core and octa-core cascade SVM systems were developed using the proposed SVM learning unit to expedite the on-chip training process. The runtime and energy efficiency of the cascade SVM systems improved with an increase in the number of cores. Interestingly, an average speedup of 421x in training time and a remarkable energy reduction of 24,497x were observed for the octa-core cascade SVM system when compared with the software SVM solution running on Intel Core i5-5257U processor. Moreover, the proposed octa-core cascade SVM system showed 73.75% and 65.78% lower area and power, respectively, than those of state-of-the-art cascade SVM architecture.

中文翻译：

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具有片上训练和分类能力的级联支持向量机的硬件高效 VLSI 设计

对于许多实时嵌入式应用程序，本地处理机器学习算法（如支持向量机 (SVM)）比云更受欢迎。然而，除了吞吐量和精度要求之外，此类嵌入式系统通常还具有严格的能量限制。因此，用于计算 SVM 的硬件高效设计对于启用这些应用程序至关重要。在本文中，使用减少的乘法次数和近似计算技术提出了一种硬件高效的 SVM 学习单元。与使用全乘法器的设计相比，这些设计技术帮助学习单元实现了 46.97% 和 35.72% 的面积和功率减少。建议的 SVM 学习单元支持片上训练和分类。节能双核，使用建议的 SVM 学习单元开发了四核和八核级联 SVM 系统，以加快片上训练过程。级联 SVM 系统的运行时间和能源效率随着内核数量的增加而提高。有趣的是，与在英特尔酷睿 i5-5257U 处理器上运行的软件 SVM 解决方案相比，八核级联 SVM 系统的训练时间平均加速了 421 倍，能耗显着降低了 24,497 倍。此外，与最先进的级联 SVM 架构相比，所提出的八核级联 SVM 系统的面积和功耗分别降低了 73.75% 和 65.78%。与在英特尔酷睿 i5-5257U 处理器上运行的软件 SVM 解决方案相比，八核级联 SVM 系统的训练时间平均加速了 421 倍，能耗显着降低了 24,497 倍。此外，与最先进的级联 SVM 架构相比，所提出的八核级联 SVM 系统的面积和功耗分别降低了 73.75% 和 65.78%。与在英特尔酷睿 i5-5257U 处理器上运行的软件 SVM 解决方案相比，八核级联 SVM 系统的训练时间平均加速了 421 倍，能耗显着降低了 24,497 倍。此外，与最先进的级联 SVM 架构相比，所提出的八核级联 SVM 系统的面积和功耗分别降低了 73.75% 和 65.78%。