43 citations · 150 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
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…
ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning
Joshua Klein, Irem Boybat, Yasir Qureshi +6
Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate…