Name
Title | ||
|---|---|---|
CHIMERA: A 0.92-TOPS, 2.2-TOPS/W Edge AI Accelerator With 2-MByte On-Chip Foundry Resistive RAM for Efficient Training and Inference |
Abstract | ||
|---|---|---|
Implementing edge artificial intelligence (AI) inference and training is challenging with current memory technologies. As deep neural networks (DNNs) grow in size, this problem is only getting worse. This article presents CHIMERA, the first non-volatile DNN chip for both edge AI training and inference using foundry on-chip resistive RAM (RRAM) macros and no off-chip memory, fabricated in 40-nm CMO... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JSSC.2022.3140753 | IEEE Journal of Solid-State Circuits |
Keywords | DocType | Volume |
Training,Random access memory,System-on-chip,Computer architecture,Convolution,Artificial intelligence,Resistive RAM | Journal | 57 |
Issue | ISSN | Citations |
4 | 0018-9200 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |