233 citations · 882 across the 34 of their papers we have counts for
37 papers
Guess & Sketch: Language Model Guided Transpilation
Celine Lee, Abdulrahman Mahmoud, Michal Kurek +5
Maintaining legacy software requires many software and systems engineering hours. Assembly code programs, which demand low-level control over the computer machine state and have no…
INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
Yuji Chai, John Gkountouras, Glenn G. Ko +2
We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models. First, we develop an extremely m…
S: Increasing GPU Utilization during Generative Inference for Higher Throughput
Yunho Jin, Chun-Feng Wu, David Brooks +1
Generating texts with a large language model (LLM) consumes massive amounts of memory. Apart from the already-large model parameters, the key/value (KV) cache that holds informatio…
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices
Yuji Chai, Devashree Tripathy, Chuteng Zhou +6
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware p…
Architectural Implications of Embedding Dimension during GCN on CPU and GPU
Matthew Adiletta, David Brooks, Gu-Yeon Wei
Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely u…
SpeedLimit: Neural Architecture Search for Quantized Transformer Models
Yuji Chai, Luke Bailey, Yunho Jin +5
While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often n…