55 citations · 102 across the 17 of their papers we have counts for
16 papers · 1 filter
RobustNCL: Robustifying Neuromorphic Continual Learning under Transient Faults with Lightweight Detect-and-Cure Strategy
Rachmad Vidya Wicaksana Putra, Shuochen Zhou, Muhammad Shafique
In neural networks, continual learning (CL) is a prominent approach for enabling deployed systems to adapt to dynamic operational environments by effectively learning from new unse…
ENCL: Enabling Efficient Neuromorphic Continual Learning with Local Learning Rule and Sub-Network Freezing Strategy
Rachmad Vidya Wicaksana Putra, Shuochen Zhou, Hassan Raza +1
Continual learning (CL) in neural networks is a promising learning approach for updating the knowledge of deployed systems based on new data encountered at run time, that have not…
AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique
Spiking Vision Transformers (SViTs) have emerged as alternative low-power ViT models, but their large sizes hinder their deployments on resource-constrained embedded AI systems. To…
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression. State-of-the-art works compress SViT…
QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique
Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (e.g., accuracy) and capabilities in ge…
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique
The rising demand for energy-efficient edge AI systems (e.g., mobile agents/robots) has increased the interest in neuromorphic computing, since it offers ultra-low power/energy AI…
Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents
Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique
Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. Recent works employ reinforcement learn…
QSViT: A Methodology for Quantizing Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique
Vision Transformer (ViT)-based models have shown state-of-the-art performance (e.g., accuracy) in vision-based AI tasks. However, realizing their capability in resource-constrained…
Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems
Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad +2
Neuromorphic Continual Learning (NCL) paradigm leverages Spiking Neural Networks (SNNs) to enable continual learning (CL) capabilities for AI systems to adapt to dynamically changi…
Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents
Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique
Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agen…
FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data through Learning Rate Enhancements for Autonomous Embedded Systems
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio +1
Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfi…
NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Intelligent mobile agents (e.g., UGVs and UAVs) typically demand low power/energy consumption when solving their machine learning (ML)-based tasks, since they are usually powered b…
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio +1
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/s…
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Networks (SNNs) can offer ultra-low power/energy consumption for machine learning-based application tasks due to their sparse spike-based operations. Currently, most…
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Embedded AI systems are expected to incur low power/energy consumption for solving machine learning tasks, as these systems are usually power constrained (e.g., object recognition…
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs) without decreasing the accuracy significantly is quantization. However, the state-of-the-a…