Uint-Packing: Multiply Your DNN Accelerator Performance via Unsigned Integer DSP Packing
Jingwei Zhang, Meng Zhang, Xinye Cao, Guoqing Li
Abstract
DSP blocks are undoubtedly efficient solutions for implementing multiply-accumulate (MAC) operations on FPGA. Since DSP resources are scarce in FPGA, the advanced solution is to pack parallel multiplication operations into a single DSP. However, available methods are based on signed-type multiplication, leading to both loss of accuracy and increased area. To solve these issues simultaneously, we propose an unsigned integer DSP packing generalization model called uint-packing. Guided by this generalization model, we design the novel computational structure of the DNN accelerator. Our system design is state-of-the-art, with 2.8× throughput and 4× energy efficiency compared to the third-place DAC-SDC’22 design.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Related papers
- XtraMAC: An Efficient MAC Architecture for Mixed-Precision LLM Inference on FPGAFeng Yu, Hongshi Tan, Yao Chen, Weng-Fai Wong et al.ISCA 2026
- WinoGen: A Highly Configurable Winograd Convolution IP Generator for Efficient CNN Acceleration on FPGAMingjun Li, Pengjia Li, Shuo Yin, Shixin Chen et al.DAC 2024 · 6 citations
- HiPACK: Efficient Sub-8-Bit Direct Convolution with SIMD and Bitwise ManagementYao Chen, Cheng Gong, Bingsheng HeMICRO 2025 · 2 citations
- DeepBurning-SEG: Generating DNN Accelerators of Segment-Grained Pipeline ArchitectureXuyi Cai, Ying Wang, Xiaohan Ma, Yinhe Han et al.MICRO 2022 · 25 citations
- HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and ImplementationHanchen Ye, Xiaofan Zhang, Zhize Huang, Gengsheng Chen et al.DAC 2020 · 72 citations
