1.9k citations · 2.4k across the 10 of their papers we have counts for
11 papers
Collapsible Linear Blocks for Super-Efficient Super Resolution
Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil +6
With the advent of smart devices that support 4K and 8K resolution, Single Image Super Resolution (SISR) has become an important computer vision problem. However, most super resolu…
EdgeAI: A Vision for Deep Learning in IoT Era
Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new par…
Dream Distillation: A Data-Independent Model Compression Framework
Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
Model compression is eminently suited for deploying deep learning on IoT-devices. However, existing model compression techniques rely on access to the original or some alternate da…
Rethinking Machine Learning Development and Deployment for Edge Devices
Liangzhen Lai, Naveen Suda
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As…
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai +3
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data…
Not All Ops Are Created Equal!
Liangzhen Lai, Naveen Suda, Vikas Chandra
Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices. In this work, we point out that typical design metrics for gau…
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
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 f…
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Hardik Sharma, Jongse Park, Naveen Suda +5
Fully realizing the potential of acceleration for Deep Neural Networks (DNNs) requires understanding and leveraging algorithmic properties. This paper builds upon the algorithmic i…
Hello Edge: Keyword Spotting on Microcontrollers
Yundong Zhang, Naveen Suda, Liangzhen Lai +1
Keyword spotting (KWS) is a critical component for enabling speech based user interactions on smart devices. It requires real-time response and high accuracy for good user experien…
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
Meng Li, Liangzhen Lai, Naveen Suda +2
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based traini…
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
Liangzhen Lai, Naveen Suda, Vikas Chandra
Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problem…