125 citations · 175 across the 12 of their papers we have counts for
12 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…
Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models
Toluwanimi O. Odemuyiwa, John D. Owens, Joel S. Emer +1
Mamba is an emerging, complex workload with various short-range and long-range dependencies, nonlinearities, and elementwise computations that are unable to run at near-peak speeds…
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…
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…
FuseMax: Leveraging Extended Einsums to Optimize Attention Accelerator Design
Nandeeka Nayak, Xinrui Wu, Toluwanimi O. Odemuyiwa +3
Attention for transformers is a critical workload that has recently received significant "attention" as a target for custom acceleration. Yet, while prior work succeeds in reducing…