3 citations · 4 across the 4 of their papers we have counts for
4 papers
Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference
Pedro Antunes, Muhammad Ihsan Al Hafiz, Jonah Ekelund +4
Space missions increasingly deploy high-fidelity sensors that produce data volumes exceeding onboard buffering and downlink capacity. This work evaluates FPGA acceleration of neura…
Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner +2
Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overpara…
Efficient Implementation of CRYSTALS-KYBER Key Encapsulation Mechanism on ESP32
Fabian Segatz, Muhammad Ihsan Al Hafiz
Kyber, an IND-CCA2-secure lattice-based post-quantum key-encapsulation mechanism, is the winner of the first post-quantum cryptography standardization process of the US National In…
A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks
Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner +2
Brain-inspired algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-inspired systems can f…