1 citations · 6 across the 34 of their papers we have counts for
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P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats
Yuzong Chen, Chao Fang, Xilai Dai +4
The substantial memory bandwidth and computational demands of large language models (LLMs) present critical challenges for efficient inference. To tackle this, the literature has e…
Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference
Chun-Ting Chen, HanGyeol Mun, Jian Meng +2
Edge inference for large language models (LLM) offers secure, low-latency, and cost-effective inference solutions. We emphasize that an edge accelerator should achieve high area ef…
Kratos: An FPGA Benchmark for Unrolled DNNs with Fine-Grained Sparsity and Mixed Precision
Xilai Dai, Yuzong Chen, Mohamed S. Abdelfattah
FPGAs offer a flexible platform for accelerating deep neural network (DNN) inference, particularly for non-uniform workloads featuring fine-grained unstructured sparsity and mixed…
M4BRAM: Mixed-Precision Matrix-Matrix Multiplication in FPGA Block RAMs
Yuzong Chen, Jordan Dotzel, Mohamed S. Abdelfattah
Mixed-precision quantization is a popular approach for compressing deep neural networks (DNNs). However, it is challenging to scale the performance efficiently with mixed-precision…
BRAMAC: Compute-in-BRAM Architectures for Multiply-Accumulate on FPGAs
Yuzong Chen, Mohamed S. Abdelfattah
Deep neural network (DNN) inference using reduced integer precision has been shown to achieve significant improvements in memory utilization and compute throughput with little or n…