221 citations · 395 across the 21 of their papers we have counts for
21 papers
DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics
Yoonsung Kim, Changhun Oh, Jinwoo Hwang +6
Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world…
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
Shaojin Ding, David Qiu, David Rim +10
End-to-end automatic speech recognition (ASR) models have seen revolutionary quality gains with the recent development of large-scale universal speech models (USM). However, deploy…
JaxPruner: A concise library for sparsity research
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell +22
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural…
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta +13
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an…
In-Storage Domain-Specific Acceleration for Serverless Computing
Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn +8
While (1) serverless computing is emerging as a popular form of cloud execution, datacenters are going through major changes: (2) storage dissaggregation in the system infrastructu…
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
Yucheng Lu, Shivani Agrawal, Suvinay Subramanian +3
Recent innovations on hardware (e.g. Nvidia A100) have motivated learning N:M structured sparsity masks from scratch for fast model inference. However, state-of-the-art learning re…
Learning Performance-Improving Code Edits
Alexander Shypula, Aman Madaan, Yimeng Zeng +7
With the decline of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes r…
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
Aman Madaan, Amir Yazdanbakhsh
The past decade has witnessed dramatic gains in natural language processing and an unprecedented scaling of large language models. These developments have been accelerated by the a…
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Ondrej Sykora, Phitchaya Mangpo Phothilimthana, Charith Mendis +1
Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors with sophis…
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
Sheng-Chun Kao, Amir Yazdanbakhsh, Suvinay Subramanian +3
Sparsity has become one of the promising methods to compress and accelerate Deep Neural Networks (DNNs). Among different categories of sparsity, structured sparsity has gained more…
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation
Amir Yazdanbakhsh, Ashkan Moradifirouzabadi, Zheng Li +1
As its core computation, a self-attention mechanism gauges pairwise correlations across the entire input sequence. Despite favorable performance, calculating pairwise correlations…
Accelerating Attention through Gradient-Based Learned Runtime Pruning
Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh +2
Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural Language Processing models. This attention mechanism calculates a correlation score f…
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi +2
Industry has gradually moved towards application-specific hardware accelerators in order to attain higher efficiency. While such a paradigm shift is already starting to show promis…
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks
Sheng-Chun Kao, Suvinay Subramanian, Gaurav Agrawal +2
Attention mechanisms, primarily designed to capture pairwise correlations between words, have become the backbone of machine learning, expanding beyond natural language processing…
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Yanqi Zhou, Xuanyi Dong, Berkin Akin +7
Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed m…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
Kiran Seshadri, Berkin Akin, James Laudon +2
Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss t…
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh +1
Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks…
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Soroush Ghodrati, Hardik Sharma, Sean Kinzer +5
Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs). However, mixed-signal circuitry suffers from limited range for inf…
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah +2
Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN…
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Amir Yazdanbakhsh, Hajar Falahati, Philip J. Wolfe +3
Generative Adversarial Networks (GANs) are one of the most recent deep learning models that generate synthetic data from limited genuine datasets. GANs are on the frontier as furth…