ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.
Mix and match: A novel fpga-centric deep neural network quantization framework
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SwiftChannel delivers a compressed CNN-based channel estimator with parameter-free attention running on FPGA, achieving sub-millisecond latency, 24x speedup, and 33x better energy efficiency than GPU baselines while generalizing across noise and channel profiles.
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ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA
ViM-Q delivers 4.96x speedup and 59.8x energy efficiency for Vision Mamba inference on FPGA versus a quantized GPU baseline using dynamic activation quantization, per-block APoT weights, and a pipelined SSM engine.
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SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation
SwiftChannel delivers a compressed CNN-based channel estimator with parameter-free attention running on FPGA, achieving sub-millisecond latency, 24x speedup, and 33x better energy efficiency than GPU baselines while generalizing across noise and channel profiles.