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BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing

arXiv 1806.08862 ↗First posted 22 Jun 2018cs.ARcited by 21

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Abstract (arXiv)

Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication-dependent applications can use reduced-precision integer or fixed-point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. We present BISMO, a vectorized bit-serial matrix multiplication overlay for reconfigurable computing. BISMO utilizes the excellent binary-operation performance of FPGAs to offer a matrix multiplication performance that scales with required precision and parallelism. We characterize the resource usage and performance of BISMO across a range of parameters to build a hardware cost model, and demonstrate a peak performance of 6.5 TOPS on the Xilinx PYNQ-Z1 board.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2018.

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Citing papers per year: 2021: 9; 2022: 4; 2023: 1; 2024: 4; 2025: 1; 2026: 2.

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  1. 2021Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimizationcited by 57
  2. 2022Hardware Approximate Techniques for Deep Neural Network Accelerators: A Surveycited by 39
  3. 2021MQBench: Towards Reproducible and Deployable Model Quantization Benchmarkcited by 38
  4. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  5. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  6. 2024MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantizationcited by 22
  7. 2023BAFFLE: A Baseline of Backpropagation-Free Federated Learningcited by 12
  8. 2024RefQSR: Reference-based Quantization for Image Super-Resolution Networkscited by 8
  9. 2022Mixed-Precision Neural Networks: A Surveycited by 7
  10. 2021Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantizationcited by 6
  11. 2021MWQ: Multiscale Wavelet Quantized Neural Networkscited by 4
  12. 2024xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systemscited by 4
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  1. 2021GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networkscited by 2
  2. 2021N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Corescited by 2
  3. 2022FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representationcited by 2
  4. 2021Understanding Cache Boundness of ML Operators on ARM Processorscited by 1
  5. 2021RCT: Resource Constrained Training for Edge AIcited by 1
  6. 2024ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNscited by 1
  7. 2025Karatsuba Matrix Multiplication and its Efficient Custom Hardware Implementationscited by 0
  8. 2026Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Acceleratorscited by 0
  9. 2026TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Modelscited by 0