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)1promises to meet such demand by storing AI model weights in dense, anal...
In the era of big data and artificial intelligence, hardware advancement in throughput and energy efficiency is essential for both cloud and edge computations. Because of the merged data storage and computing units, compute-in-memory is becoming one of t
Resistive RAM (ReRAM) technology has emerged as an attractive alternative to embedded flash memory storage at advanced nodes. Indeed, multiple foundries are offering ReRAM IP arrays at 40nm nodes, and below. ReRAM has very attractive characteristics, with one significant limitation: nonvola...
et al. CMOS-integrated memristive non-volatile computing-in-memory for AI edge processors. Nat. Electron. 2, 420–428 (2019). Article Google Scholar Mochida, R. et al. A 4M synapses integrated analog ReRAM based 66.5 TOPS/W neural-network processor with cell current controlled writing and...
Many computing applications - including\nmachine learning - can benefit from\nanalogue computing performed in-memory\nusing non-volatile devices such as resistive\nrandom-access memory (ReRAM). The\napproach reduces the area and power\nrequired compared with using typical\ndigital circuits. However...
Open communities sharing the effort for building common tools, prototypes, business workflows and standardizations are critical to accelerate a Chiplet Economy,” said Tom Hackenberg, Principal Analyst, Computing & Software Semiconductor, Memory and Computing Division, Yole Group About the Open Compute ...
et al. 24.1 A 1Mb multibit ReRAM computing-in-memory macro with 14.6ns parallel MAC computing time for CNN based AI edge processors. In 2019 IEEE International Solid-State Circuits Conference (ISSCC) Digest of Technical Papers 388–390 (IEEE, 2019). Chen, W.-H. et al. CMOS-integrated ...
Compute-in-memory (CIM) accelerators based on emerging memory devices are of potential use in edge artificial intelligence and machine learning applications due to their power and performance capabilities. However, the privacy and security of CIM acceler
Finally, prospects and the future of CIM for numerical computations with high accuracy are discussed. Keywords: compute-in-memory (CIM); numerical computations; resistive random-access memory (ReRAM); partial differential equations (PDEs); crossbar...
To tackle the issue of the challenges associated with adapting traditional CIM accelerators’ self-attention modules for in-memory computation, Figure 1d illustrates our proposed solution, which employs product quantization to cluster one operand of the MatMul into a codebook, pre-written to the NVM ...