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Found 11 papers, 5 papers with code
TinyCL: An Efficient Hardware Architecture for Continual Learning on Autonomous Systems
Towards this, we design a hardware architecture, TinyCL, to perform CL on resource-constrained autonomous systems.
SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
In particular, fixed-point quantization is desirable to ease the computations using lightweight blocks, like adders and multipliers, of the underlying hardware.
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomous, and interconnected systems.
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks
The high computational complexity of these networks, which translates to increased energy consumption, is the foremost obstacle towards deploying large DNNs in resource-constrained systems.
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations
In our experiments, we evaluate tradeoffs between area, power consumption, and critical path delay of the designs implemented with the ASIC design flow, and the accuracy of the quantized CapsNets, compared to the exact functions.
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS).
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks
Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are vulnerable to security threats, such as adversarial attacks, i. e., small perturbations added to the input for inducing a misclassification.
CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor
Our best experiment achieves an accuracy on offline implementation of 86%, that drops to 83% when it is ported onto the Loihi Chip.
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life.
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs.
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks
The goal is to reduce the hardware requirements of CapsNets by removing unused/redundant connections and capsules, while keeping high accuracy through tests of different learning rate policies and batch sizes.
