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Found 12 papers, 4 papers with code
Towards Open-World Gesture Recognition
We propose leveraging continual learning to make machine learning models adaptive to new tasks without degrading performance on previously learned tasks.
EdgeAI: A Vision for Deep Learning in IoT Era
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices.
Dream Distillation: A Data-Independent Model Compression Framework
Model compression is eminently suited for deploying deep learning on IoT-devices.
Rethinking Machine Learning Development and Deployment for Edge Devices
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.
Federated Learning with Non-IID Data
Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
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.
Not All Ops Are Created Equal!
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
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
We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.
Ranked #13 on
Keyword Spotting
on Google Speech Commands
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints.
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations.
