Name
Title | ||
|---|---|---|
A Pragmatic Approach To On-Device Incremental Learning System With Selective Weight Updates |
Abstract | ||
|---|---|---|
Incremental learning is drawing attention to widen capabilities of device-AI. Previous works have researched to reduce numerous computations and memory accesses required for the training process of IL, but they could not show a noticeable improvement in the weight gradient computation (WGC) phase. Therefore, we propose a selective weight update technique that searches for critical weights to be updated by applying the IL algorithm that training per-task binary masks. Also, we introduce a novel dataflow for the implementation of selective WGC on typical NPUs with minimum overheads. On average, our system shows a 2.9 x speed up and 2.5x energy efficiency in WGC without degrading training quality. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DAC18072.2020.9218507 | PROCEEDINGS OF THE 2020 57TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
DocType | ISSN | Citations |
Conference | 0738-100X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaekang Shin | 1 | 3 | 1.75 |
| Seungkyu Choi | 2 | 10 | 3.90 |
| Yeongjae Choi | 3 | 45 | 5.78 |
| Lee-Sup Kim | 4 | 707 | 98.58 |