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WWW: What, When, Where to Compute-in-Memory
The proposed work provides insights into what type of CiM to use, and when and where to optimally integrate it in the cache hierarchy for GEMM acceleration.
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction.
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Processing in memory has been proposed as a promising solution for the memory wall bottleneck for ML workloads.
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck.
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