Abstract
We present a low-latency DNN compression technique to reduce DRAM energy, significant in DNN inferences, namely Outlier-Aware Approximation (O-2A) coding. This technique compresses 8-bit integer, de-facto standard of DNN inferences, to 6-bit without degrading the accuracies of DNNs. The hardware for the O-2A coding can be easily embedded to DRAM controllers due to small overhead. In an Eyeriss platform, the O-2A coding improves both DRAM energy and system performance by 18~20%. The O-2A coding enables us to implement an error-correction scheme without additional parity overhead, opening the possibility of an approximate DRAM to simultaneously reduce DRAM accessing and refresh energy.
| Original language | English |
|---|---|
| Title of host publication | 2020 57th ACM/IEEE Design Automation Conference, DAC 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781450367257 |
| DOIs | |
| Publication status | Published - Jul 2020 |
| Event | 57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States Duration: 20 Jul 2020 → 24 Jul 2020 |
Publication series
| Name | Proceedings - Design Automation Conference |
|---|---|
| Volume | 2020-July |
| ISSN (Print) | 0738-100X |
Conference
| Conference | 57th ACM/IEEE Design Automation Conference, DAC 2020 |
|---|---|
| Country/Territory | United States |
| City | Virtual, San Francisco |
| Period | 20/07/20 → 24/07/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Approximation
- DRAM
- Low-latency DNN compression
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