28 citations · 41 across the 4 of their papers we have counts for
5 papers
Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
Alberto Pepe, Chien-Yu Lin, Despoina Magka +6
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Com…
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…
Tailors: Accelerating Sparse Tensor Algebra by Overbooking Buffer Capacity
Zi Yu Xue, Yannan Nellie Wu, Joel S. Emer +1
Sparse tensor algebra is a challenging class of workloads to accelerate due to low arithmetic intensity and varying sparsity patterns. Prior sparse tensor algebra accelerators have…
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan +3
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse spar…
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
Yannan Nellie Wu, Po-An Tsai, Angshuman Parashar +2
In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single…