2 citations · 2 across the 2 of their papers we have counts for
8 papers
TileLoom: Automatic Dataflow Planning for Tile-Based Languages on Spatial Dataflow Accelerators
Wei Li, Zhenyu Bai, Heru Wang +6
Spatial dataflow accelerators are a promising direction for next-generation computer systems because they can reduce the memory bottlenecks of traditional von Neumann machines such…
Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives
Zhanglu Yan, Zhenyu Bai, Weng-Fai Wong +1
Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. Ho…
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Zhanglu Yan, Zhenyu Bai, Tulika Mitra +1
Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) off…
A Data-Driven Dynamic Execution Orchestration Architecture
Zhenyu Bai, Pranav Dangi, Rohan Juneja +4
Domain-specific accelerators deliver exceptional performance on their target workloads through fabrication-time orchestrated datapaths. However, such specialized architectures ofte…
Matterhorn: Masked Time-to-First-Spike Encoding by Reassigning the Silent State for Sparse and Energy-Efficient Spiking Transformers
Zhanglu Yan, Kaiwen Tang, Zixuan Zhu +4
Spiking neural networks (SNNs) promise energy-efficient inference for large language models (LLMs), yet most reported savings rely on compute-operation counts that overlook data mo…
TerEffic: Highly Efficient Ternary LLM Inference on FPGA
Chenyang Yin, Zhenyu Bai, Pranav Venkatram +3
Deploying Large Language Models (LLMs) efficiently on edge devices is often constrained by limited memory capacity and high power consumption. Low-bit quantization methods, particu…