MOUSE: Inference In Non-volatile Memory for Energy Harvesting Applications
MOUSE: Inference In Non-volatile Memory for Energy Harvesting Applications
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DOI：
10.1109/micro50266.2020.00042
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发表时间：
2020-10
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通讯作者：
Salonik Resch;S. K. Khatamifard;Z. Chowdhury;Masoud Zabihi;Zhengyang Zhao;Hüsrev Cılasun;Jianping Wang;S. Sapatnekar;Ulya R. Karpuzcu
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作者：
Salonik Resch;S. K. Khatamifard;Z. Chowdhury;Masoud Zabihi;Zhengyang Zhao;Hüsrev Cılasun;Jianping Wang;S. Sapatnekar;Ulya R. Karpuzcu
将机器学习功能引入低功耗设备的需求日益增长。通过将机器学习的计算能力与低功耗设备的部署能力相结合，许多新的应用程序成为可能。在某些应用中，此类设备甚至没有电池，必须完全依赖能量收集技术。这对硬件造成了极大的限制，硬件必须高效节能，并且能够承受因断电而造成的中断。在这里，我们提出了一种利用非易失性自旋电子存储器的内存中机器学习加速器。内存处理和非易失性的结合提供了一个关键优势，即每次操作后都会有效保存进度。这使得能够以最小的开销实现即时关闭和重启功能。此外，该操作具有高能效，从而降低功耗。
There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities of low power devices, a number of new applications become possible. In some applications, such devices will not even have a battery, and must rely solely on energy harvesting techniques. This puts extreme constraints on the hardware, which must be energy efficient and capable of tolerating interruptions due to power outages. Here, we propose an in-memory machine learning accelerator utilizing non-volatile spintronic memory. The combination of processing-in-memory and non-volatility provides a key advantage in that progress is effectively saved after every operation. This enables instant shut down and restart capabilities with minimal overhead. Additionally, the operations are highly energy efficient leading to low power consumption.