3 papers
cs.AR2026
The Turbo-Charged Mapper: Fast and Optimal Mapping for Energy-efficient and Low-latency Accelerator Design
Michael Gilbert, Tanner Andrulis, Vivienne Sze +1
The energy and latency of an accelerator running a deep neural network (DNN) depend on how the computation and data movement are scheduled in the accelerator (i.e., mapping), and p…
cs.AR2026
Fast and Fusiest: An Optimal Fusion-Aware Mapper for Accelerator Design
Tanner Andrulis, Michael Gilbert, Vivienne Sze +1
A low-latency and energy-efficient tensor algebra accelerator design must optimize how data movement and operations are scheduled (i.e., mapped) in the accelerator architecture. A…
cs.AR2024
LoopTree: Exploring the Fused-layer Dataflow Accelerator Design Space
Michael Gilbert, Yannan Nellie Wu, Joel S. Emer +1
Latency and energy consumption are key metrics in the performance of deep neural network (DNN) accelerators. A significant factor contributing to latency and energy is data transfe…