Name
Abstract | ||
|---|---|---|
ABSTRACTCompute in-memory (CIM) is a promising technique that minimizes data transport, maximizes memory throughput, and performs computation on the bitline of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we explore the impact of device variation (calibrated with measured data on foundry RRAM arrays) and propose a new algorithm based on device variation to increase both performance and accuracy for CIM designs. We demonstrate up to 36% power improvement and 44% performance improvement, while satisfying any error constraint. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISLPED52811.2021.9502484 | ISLPED |
Keywords | DocType | ISSN |
eNVM,RRAM,compute in-memory technique,CIM designs,CMOS architectures,neural networks,resistive random access memory,embedded nonvolatile memories,device variation,statistical optimization | Conference | 1533-4678 |
ISBN | Citations | PageRank |
978-1-6654-3923-7 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Crafton | 1 | 0 | 1.35 |
| Samuel Spetalnick | 2 | 0 | 1.69 |
| Jong-Hyeok Yoon | 3 | 2 | 1.66 |
| Arijit Raychowdhury | 4 | 284 | 48.04 |