Applying quantization-aware training with column-wise scale factors for both weights and partial sums improves CIM accelerator accuracy by 0.99 to 2.69 percentage points over reported baselines.
ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AR 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators
Applying quantization-aware training with column-wise scale factors for both weights and partial sums improves CIM accelerator accuracy by 0.99 to 2.69 percentage points over reported baselines.