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08/21/2023
Jumping through Local Minima: Quantization in the Loss Landscape of Vision Transformers
Quantization scale and bit-width are the most important parameters when ...
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01/26/2023
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices
The ability to accurately predict deep neural network (DNN) inference pe...
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11/17/2022
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers
When considering post-training quantization, prior work has typically fo...
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08/17/2022
Restructurable Activation Networks
Is it possible to restructure the non-linear activation functions in a d...
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12/29/2021
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge
Autonomous systems are highly vulnerable to a variety of adversarial att...
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03/17/2021
Collapsible Linear Blocks for Super-Efficient Super Resolution
With the advent of smart devices that support 4K and 8K resolution, Sing...
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10/21/2020
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
Executing machine learning workloads locally on resource constrained mic...
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10/06/2020
Rank and run-time aware compression of NLP Applications
Sequence model based NLP applications can be large. Yet, many applicatio...
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08/03/2020
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Matrix multiplications between asymmetric bit-width operands, especially...
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07/07/2020
The gem5 Simulator: Version 20.0+
The open-source and community-supported gem5 simulator is one of the mos...
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11/04/2019
Ternary MobileNets via Per-Layer Hybrid Filter Banks
MobileNets family of computer vision neural networks have fueled tremend...
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10/04/2019
Pushing the limits of RNN Compression
Recurrent Neural Networks (RNN) can be difficult to deploy on resource c...
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06/12/2019
Run-Time Efficient RNN Compression for Inference on Edge Devices
Recurrent neural networks can be large and compute-intensive, yet many a...
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06/07/2019
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be large and compute-intensive, maki...
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03/04/2019