44 citations · 57 across the 10 of their papers we have counts for
11 papers
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
Anagha Nimbekar, Prabodh Katti, Chen Li +3
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computatio…
Bayesian Inference Accelerator for Spiking Neural Networks
Prabodh Katti, Anagha Nimbekar, Chen Li +3
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks. However, inference involving Bayesian models requires multiple instantiations…
Towards Efficient and Reliable AI Through Neuromorphic Principles
Bipin Rajendran, Osvaldo Simeone, Bashir M. Al-Hashimi
Artificial intelligence (AI) research today is largely driven by ever-larger neural network models trained on graphics processing units (GPUs). This paradigm has yielded remarkable…
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler f…
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems
Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-bas…
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open"…
Dynamic DNNs Meet Runtime Resource Management on Mobile and Embedded Platforms
Lei Xun, Bashir M. Al-Hashimi, Jonathon Hare +1
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms due to low latency and better privacy. However, efficient deployment on these pl…
Temporal Early Exits for Efficient Video Object Detection
Amin Sabet, Jonathon Hare, Bashir Al-Hashimi +1
Transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts utilised optical flow to allow unchanged features…
Optimising Resource Management for Embedded Machine Learning
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this paper,…
Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern…
Rapid Generation of Thermal-Safe Test Schedules
Paul Rosinger, Bashir Al-Hashimi, Krishnendu Chakrabarty
Overheating has been acknowledged as a major issue in testing complex SOCs. Several power constrained system-level DFT solutions (power constrained test scheduling) have recently b…