8 citations · 10 across the 10 of their papers we have counts for
10 papers
LLM-based Hardware Development with Hierarchical IRs and End-to-End Multi-Agent Workflow
Chenyang Yin, Agasthi Haputhanthri, Aditya Anirudh Jonnalagadda +7
Large language models (LLMs) are increasingly used in software development, but their use in complex hardware design remains limited. This gap stems from both the scarcity of publi…
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…
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…
Data-aware Dynamic Execution of Irregular Workloads on Heterogeneous Systems
Zhenyu Bai, Dan Wu, Pranav Dangi +3
Current approaches to scheduling workloads on heterogeneous systems with specialized accelerators often rely on manual partitioning, offloading tasks with specific compute patterns…
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…
Enhancing CGRA Efficiency Through Aligned Compute and Communication Provisioning
Zhaoying Li, Pranav Dangi, Chenyang Yin +5
Coarse-grained Reconfigurable Arrays (CGRAs) are domain-agnostic accelerators that enhance the energy efficiency of resource-constrained edge devices. The CGRA landscape is diverse…
Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives
Zhanglu Yan, Zhenyu Bai, Kaiwen Tang +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…
SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs
Zhenyu Bai, Pranav Dangi, Huize Li +1
Efficiently supporting long context length is crucial for Transformer models. The quadratic complexity of the self-attention computation plagues traditional Transformers. Sliding w…