45 citations · 83 across the 7 of their papers we have counts for
3 papers · 1 filter
AccelForge: Comprehensive Modeling and Co-Design Framework for AI Accelerators
Tanner Andrulis, Michael Gilbert, Vivienne Sze +1
Tensor algebra workloads, of which deep neural networks are prominent examples, are energy-intensive workloads in modern datacenter and edge deployments, making accelerators necess…
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…
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…