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04/27/2023
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sp...
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03/30/2023
Self-Refine: Iterative Refinement with Self-Feedback
Like people, LLMs do not always generate the best text for a given gener...
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03/06/2023
Domain-Specific Computational Storage for Serverless Computing
While (1) serverless computing is emerging as a popular form of cloud ex...
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02/15/2023
Learning Performance-Improving Code Edits
The waning of Moore's Law has shifted the focus of the tech industry tow...
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10/08/2022
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Analytical hardware performance models yield swift estimation of desired...
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09/16/2022
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
Reasoning is a key pillar of human cognition and intelligence. In the pa...
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09/15/2022
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
Sparsity has become one of the promising methods to compress and acceler...
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09/01/2022
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation
As its core computation, a self-attention mechanism gauges pairwise corr...
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04/07/2022
Accelerating Attention through Gradient-Based Learned Runtime Pruning
Self-attention is a key enabler of state-of-art accuracy for various tra...
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10/20/2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Industry has gradually moved towards application-specific hardware accel...
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02/20/2021
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
Edge TPUs are a domain of accelerators for low-power, edge devices and a...
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02/17/2021
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Neural architectures and hardware accelerators have been two driving for...
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02/02/2021
Apollo: Transferable Architecture Exploration
The looming end of Moore's Law and ascending use of deep learning drives...
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01/23/2020
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Achieving faster execution with shorter compilation time can foster furt...
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06/27/2019
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Low-power potential of mixed-signal design makes it an alluring option t...
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11/05/2018
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Despite numerous state-of-the-art applications of Deep Neural Networks (...
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05/10/2018
