Papers › CoQMoE: Co-Designed Quantization and Computation Orchestration for Mixture-of-Experts...
CoQMoE: Co-Designed Quantization and Computation Orchestration for Mixture-of-Experts Vision Transformer on FPGA
Jiale Dong, Hao Wu, Zihao Wang, Wenqi Lou, Zhendong Zheng, Lei Gong, Chao Wang, Xuehai Zhou
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Vision Transformers (ViTs) exhibit superior performance in computer vision tasks but face deployment challenges on resource-constrained devices due to high computational/memory demands. While Mixture-of-Experts Vision Transformers (MoE-ViTs) mitigate this through a scalable architecture with sub-linear computational growth, their hardware implementation on FPGAs remains constrained by resource limitations. This paper proposes a novel accelerator for efficiently implementing quantized MoE models on FPGAs through two key innovations: (1) A dual-stage quantization scheme combining precision-preserving complex quantizers with hardware-friendly simplified quantizers via scale reparameterization, with only 0.28 % accuracy loss compared to full precision; (2) A resource-aware accelerator architecture featuring latency-optimized streaming attention kernels and reusable linear operators, effectively balancing performance and resource consumption. Experimental results demonstrate that our accelerator achieves nearly 155 frames per second, a 5.35× improvement in throughput, and over 80% energy reduction compared to state-of-the-art (SOTA) FPGA MoE accelerators, while maintaining <1% accuracy loss across vision benchmarks. Our implementation is available at https://github.com/DJ000011/CoQMoE.
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