A compute-in-memory chip based on resistive random-access memory
University of California San Diego · Stanford University · +4 more institutions
Abstract
Abstract Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) 1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory 2–5 . Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware 6–17 , it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to…
Citation impact
- FWCI
- 65.36
- Percentile
- 100%
- References
- 62
Authors
14Topics & keywords
- Resistive random-access memory
- Computer science
- Efficient energy use
- Inference
- Abstraction
- Software
- Chip
- Computer architecture
- Affordable and clean energy