595 citations · 1.9k across the 91 of their papers we have counts for
16 papers · 1 filter
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
Modeling and Optimizing Performance Bottlenecks for Neuromorphic Accelerators
Jason Yik, Walter Gallego Gomez, Andrew Cheng +8
Neuromorphic accelerators offer promising platforms for machine learning (ML) inference by leveraging event-driven, spatially-expanded architectures that naturally exploit unstruct…
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh +5
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correc…
Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
Korneel Van den Berghe, Stein Stroobants, Vijay Janapa Reddi +1
Neuromorphic computing systems are set to revolutionize energy-constrained robotics by achieving orders-of-magnitude efficiency gains, while enabling native temporal processing. Sp…
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Jason Jabbour, Dong-Ki Kim, Max Smith +6
Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C constraints. Compression techniques such…
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder +24
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…