Local adaptive illumination-driven input-level fusion for infrared and visible object detection
Remote sensing object detection based on the combination of infrared and visible images
can effectively adapt to the around-the-clock and changeable illumination conditions …
can effectively adapt to the around-the-clock and changeable illumination conditions …
Compiling KB-sized machine learning models to tiny IoT devices
Recent advances in machine learning (ML) have produced KiloByte-size models that can
directly run on constrained IoT devices. This approach avoids expensive communication …
directly run on constrained IoT devices. This approach avoids expensive communication …
Memristor based binary convolutional neural network architecture with configurable neurons
L Huang, J Diao, H Nie, W Wang, Z Li, Q Li… - Frontiers in …, 2021 - frontiersin.org
The memristor-based convolutional neural network (CNN) gives full play to the advantages
of memristive devices, such as low power consumption, high integration density, and strong …
of memristive devices, such as low power consumption, high integration density, and strong …
A neural network prefetcher for arbitrary memory access patterns
Memory prefetchers are designed to identify and prefetch specific access patterns, including
spatiotemporal locality (eg, strides, streams), recurring patterns (eg, varying strides, temporal …
spatiotemporal locality (eg, strides, streams), recurring patterns (eg, varying strides, temporal …
A hardware-friendly low-bit power-of-two quantization method for cnns and its fpga implementation
X Sui, Q Lv, Y Bai, B Zhu, L Zhi, Y Yang, Z Tan - Sensors, 2022 - mdpi.com
To address the problems of convolutional neural networks (CNNs) consuming more
hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency …
hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency …
Design and optimization of energy-accuracy tradeoff networks for mobile platforms via pretrained deep models
Many real-world edge applications including object detection, robotics, and smart health are
enabled by deploying deep neural networks (DNNs) on energy-constrained mobile …
enabled by deploying deep neural networks (DNNs) on energy-constrained mobile …
Lightweight convolution neural networks for mobile edge computing in transportation cyber physical systems
Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of
enhanced computing and storage capability via offloading computing tasks to remote cloud …
enhanced computing and storage capability via offloading computing tasks to remote cloud …
HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks
Binary neural network (BNN), where both the weight and the activation values are
represented with one bit, provides an attractive alternative to deploy highly efficient deep …
represented with one bit, provides an attractive alternative to deploy highly efficient deep …
An efficient CNN accelerator for low-cost edge systems
K Choi, GE Sobelman - ACM Transactions on Embedded Computing …, 2022 - dl.acm.org
Customized hardware based convolutional neural network (CNN or ConvNet) accelerators
have attracted significant attention for applications in a low-cost, edge computing system …
have attracted significant attention for applications in a low-cost, edge computing system …
Towards fast and energy-efficient binarized neural network inference on fpga
Binarized Neural Network (BNN) removes bitwidth redundancy in classical CNN by using a
single bit (-1/+ 1) for network parameters and intermediate representations, which has …
single bit (-1/+ 1) for network parameters and intermediate representations, which has …