5 papers
SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
Qunyou Liu, Pengbo Yu, Marina Zapater +1
Deep neural networks (DNNs) are essential for performing advanced tasks on edge or mobile devices, yet their deployment is often hindered by severe resource constraints, including…
GreenLLM: SLO-Aware Dynamic Frequency Scaling for Energy-Efficient LLM Serving
Qunyou Liu, Darong Huang, Marina Zapater +1
Large Language Models (LLMs) are becoming the backbone of modern cloud services, yet their inference costs are dominated by GPU energy. Unlike traditional GPU workloads, LLM infere…
Gem5-AcceSys: Enabling System-Level Exploration of Standard Interconnects for Novel Accelerators
Qunyou Liu, Marina Zapater, David Atienza
The growing demand for efficient, high-performance processing in machine learning (ML) and image processing has made hardware accelerators, such as GPUs and Data Streaming Accelera…
LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles
Corey Lammie, Yuxuan Wang, Flavio Ponzina +7
When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learnin…
MatrixFlow: System-Accelerator co-design for high-performance transformer applications
Qunyou Liu, Marina Zapater, David Atienza
Transformers are central to advances in artificial intelligence (AI), excelling in fields ranging from computer vision to natural language processing. Despite their success, their…