Search Results for author: Vikas Chandra

Found 40 papers, 13 papers with code

PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion

no code implementations12 Dec 2022 Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra

PathFusion introduces a path consistency loss between shallow and deep features, which encourages the 2D backbone and its fusion path to transform 2D features in a way that is semantically aligned with the transform of the 3D backbone.

SDRM3: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads

no code implementations7 Dec 2022 Seah Kim, Hyoukjun Kwon, Jinook Song, Jihyuck Jo, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra

Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control often involve dynamic behaviors in various levels; task, model, and layers (or, ML operators) within a model.


Fast Point Cloud Generation with Straight Flows

no code implementations4 Dec 2022 Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu

We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods.

Point Cloud Completion text-guided-generation

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

1 code implementation2 Jun 2022 Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

Efficient deep neural network (DNN) models equipped with compact operators (e. g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e. g., the total number of weights/operations) while maintaining a decent model accuracy.

Low-Rank+Sparse Tensor Compression for Neural Networks

no code implementations2 Nov 2021 Cole Hawkins, Haichuan Yang, Meng Li, Liangzhen Lai, Vikas Chandra

Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices.

Tensor Decomposition

Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

1 code implementation CVPR 2022 Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, David Z. Pan

Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs.

Image Classification Representation Learning +1

Contrastive Quant: Quantization Makes Stronger Contrastive Learning

no code implementations29 Sep 2021 Yonggan Fu, Qixuan Yu, Meng Li, Xu Ouyang, Vikas Chandra, Yingyan Lin

Contrastive learning, which learns visual representations by enforcing feature consistency under different augmented views, has emerged as one of the most effective unsupervised learning methods.

Contrastive Learning Quantization

NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training

1 code implementation ICLR 2022 Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra

In this work, we observe that the poor performance is due to a gradient conflict issue: the gradients of different sub-networks conflict with that of the supernet more severely in ViTs than CNNs, which leads to early saturation in training and inferior convergence.

Data Augmentation Image Classification +2

Collaborative Training of Acoustic Encoders for Speech Recognition

no code implementations16 Jun 2021 Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh, Ozlem Kalinli, Michael L. Seltzer, Vikas Chandra

On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets.

speech-recognition Speech Recognition

Vision Transformers with Patch Diversification

1 code implementation26 Apr 2021 Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

To alleviate this problem, in this work, we introduce novel loss functions in vision transformer training to explicitly encourage diversity across patch representations for more discriminative feature extraction.

Image Classification Semantic Segmentation

Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search

1 code implementation2 Mar 2021 Kartik Hegde, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, Christopher W. Fletcher

The key idea is to derive a smooth, differentiable approximation to the otherwise non-smooth, non-convex search space.

Feature-Align Network with Knowledge Distillation for Efficient Denoising

no code implementations2 Mar 2021 Lucas D. Young, Fitsum A. Reda, Rakesh Ranjan, Jon Morton, Jun Hu, Yazhu Ling, Xiaoyu Xiang, David Liu, Vikas Chandra

(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.

Efficient Neural Network Image Denoising +2

Memory-efficient Speech Recognition on Smart Devices

no code implementations23 Feb 2021 Ganesh Venkatesh, Alagappan Valliappan, Jay Mahadeokar, Yuan Shangguan, Christian Fuegen, Michael L. Seltzer, Vikas Chandra

Recurrent transducer models have emerged as a promising solution for speech recognition on the current and next generation smart devices.

speech-recognition Speech Recognition

AlphaNet: Improved Training of Supernets with Alpha-Divergence

2 code implementations16 Feb 2021 Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

Weight-sharing NAS builds a supernet that assembles all the architectures as its sub-networks and jointly trains the supernet with the sub-networks.

Image Classification Neural Architecture Search

CPT: Efficient Deep Neural Network Training via Cyclic Precision

1 code implementation ICLR 2021 Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training.

Language Modelling

EVRNet: Efficient Video Restoration on Edge Devices

no code implementations3 Dec 2020 Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra

Video transmission applications (e. g., conferencing) are gaining momentum, especially in times of global health pandemic.

Denoising SSIM +2

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

2 code implementations CVPR 2021 Dilin Wang, Meng Li, Chengyue Gong, Vikas Chandra

Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77. 3% to 80. 7% on ImageNet, and outperforms SOTA models, including BigNAS and Once-for-All networks.

Neural Architecture Search

DNA: Differentiable Network-Accelerator Co-Search

no code implementations28 Oct 2020 Yongan Zhang, Yonggan Fu, Weiwen Jiang, Chaojian Li, Haoran You, Meng Li, Vikas Chandra, Yingyan Lin

Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications.

One Weight Bitwidth to Rule Them All

no code implementations22 Aug 2020 Ting-Wu Chin, Pierce I-Jen Chuang, Vikas Chandra, Diana Marculescu

Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited.

Image Classification Model Compression +2

NASGEM: Neural Architecture Search via Graph Embedding Method

no code implementations8 Jul 2020 Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shi-Yu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran Chen

To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.

Graph Embedding Graph Similarity +3

Improving Efficiency in Neural Network Accelerator Using Operands Hamming Distance optimization

no code implementations13 Feb 2020 Meng Li, Yilei Li, Pierce Chuang, Liangzhen Lai, Vikas Chandra

Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric.

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

no code implementations10 Feb 2020 Lei Yang, Zheyu Yan, Meng Li, Hyoukjun Kwon, Liangzhen Lai, Tushar Krishna, Vikas Chandra, Weiwen Jiang, Yiyu Shi

Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).

Neural Architecture Search

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent

1 code implementation ICLR 2020 Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu

Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited.

On the Pareto Efficiency of Quantized CNN

no code implementations25 Sep 2019 Ting-Wu Chin, Pierce I-Jen Chuang, Vikas Chandra, Diana Marculescu

Weight Quantization for deep convolutional neural networks (CNNs) has shown promising results in compressing and accelerating CNN-powered applications such as semantic segmentation, gesture recognition, and scene understanding.

Gesture Recognition Quantization +2

Heterogeneous Dataflow Accelerators for Multi-DNN Workloads

no code implementations13 Sep 2019 Hyoukjun Kwon, Liangzhen Lai, Tushar Krishna, Vikas Chandra

The results suggest that HDA is an alternative class of Pareto-optimal accelerators to RDA with strength in energy, which can be a better choice than RDAs depending on the use cases.

Distributed, Parallel, and Cluster Computing

Federated Learning with Non-IID Data

1 code implementation2 Jun 2018 Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra

Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

Federated Learning

CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs

1 code implementation19 Jan 2018 Liangzhen Lai, Naveen Suda, Vikas Chandra

Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication.

Efficient Neural Network

Not All Ops Are Created Equal!

no code implementations12 Jan 2018 Liangzhen Lai, Naveen Suda, Vikas Chandra

Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices.

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks

no code implementations5 Dec 2017 Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Joon Kyung Kim, Vikas Chandra, Hadi Esmaeilzadeh

Compared to Stripes, BitFusion provides 2. 6x speedup and 3. 9x energy reduction at 45 nm node when BitFusion area and frequency are set to those of Stripes.

Hello Edge: Keyword Spotting on Microcontrollers

16 code implementations20 Nov 2017 Yundong Zhang, Naveen Suda, Liangzhen Lai, Vikas Chandra

We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.

Keyword Spotting

PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training

no code implementations ICLR 2018 Meng Li, Liangzhen Lai, Naveen Suda, Vikas Chandra, David Z. Pan

Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints.

General Classification Image Classification +1

Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations

no code implementations8 Mar 2017 Liangzhen Lai, Naveen Suda, Vikas Chandra

To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations.

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