Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MICRO50266.2020.00042 | 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) |
Keywords | DocType | ISBN |
Intermittent computing,Processing-in-Memory | Conference | 978-1-7281-7384-9 |
Citations | PageRank | References |
0 | 0.34 | 46 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salonik Resch | 1 | 8 | 4.37 |
| S. Karen Khatamifard | 2 | 7 | 1.79 |
| Zamshed I. Chowdhury | 3 | 15 | 4.45 |
| Masoud Zabihi | 4 | 14 | 4.10 |
| Zhengyang Zhao | 5 | 10 | 3.72 |
| Hüsrev Cilasun | 6 | 0 | 0.34 |
| Jian-Ping Wang | 7 | 6 | 3.26 |
| S. S. Sapatnekar | 8 | 396 | 27.16 |
| Ulya R. Karpuzcu | 9 | 277 | 22.27 |