mlsanityData to 7 Oct 2026

SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration

arXiv 2103.01308 ↗First posted 1 Mar 2021cs.LGcited by 1

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Abstract (arXiv)

Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware. We present SWIS - Shared Weight bIt Sparsity, a quantization framework for efficient neural network inference acceleration delivering improved performance and storage compression through an offline weight decomposition and scheduling algorithm. SWIS can achieve up to 54.3% (19.8%) point accuracy improvement compared to weight truncation when quantizing MobileNet-v2 to 4 (2) bits post-training (with retraining) showing the strength of leveraging shared bit-sparsity in weights. SWIS accelerator gives up to 6x speedup and 1.9x energy improvement overstate of the art bit-serial architectures.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

8 of 17 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  4. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 829
  5. 2016DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradientscited by 557
  6. 2017Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networkscited by 95
  7. 2016Bit-pragmatic Deep Neural Network Computingcited by 30
  8. 2018DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computingcited by 1

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processorscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2021Confounding Tradeoffs for Neural Network Quantization
  2. 2026SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference ↗
  3. 2021Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
  4. 2026Few Bits, One Law: Toward W2A4KV2
  5. 2022S4: a High-sparsity, High-performance AI Accelerator
  6. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
  7. 2026To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training
  8. 2021RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions