publications

2025

1. ARC 2025

   A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks

   Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner, and 2 more authors

   In Applied Reconfigurable Computing. Architectures, Tools, and Applications, 2025

   Abs

   Brain-like algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-like systems can feature local learning rules, both unsupervised/semi-supervised learning and different types of plasticity (structural/synaptic), allowing them to potentially be faster and more energy-efficient than traditional machine learning alternatives. Among the more salient brain-like algorithms are Bayesian Confidence Propagation Neural Networks (BCPNNs). BCPNN is an important tool for both machine learning and computational neuroscience research, and recent work shows that BCPNN can reach state-of-the-art performance in tasks such as learning and memory recall compared to other models. Unfortunately, BCPNN is primarily executed on slow general-purpose processors (CPUs) or power-hungry graphics processing units (GPUs), reducing the applicability of using BCPNN in Edge systems, among others. In this work, we design a reconfigurable stream-based accelerator for BCPNN using Field-Programmable Gate Arrays (FPGA) using Xilinx Vitis High-Level Synthesis (HLS) flow. Furthermore, we model our accelerator’s performance using first principles, and we empirically show that our proposed accelerator (full-featured kernel non-structural plasticity) is between 1.3x - 5.3x faster than an Nvidia A100 GPU while at the same time consuming between 2.62x - 3.19x less power and 5.8x - 16.5x less energy without any degradation in performance.

2. MCSoC 2025

   Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference

   Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner, and 2 more authors

   In 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Dec 2025

   ISSN: 2771-3075

   Abs DOI

   Edge AI increasingly requires models that learn and adapt on-device under a tight energy budget. Mainstream deep learning models, while powerful, are often overparameterized, energy-hungry and dependent on cloud connectivity. Brain-Like Neural Networks (BLNNs), such as the Bayesian Confidence Propagation Neural Network (BCPNN), propose a neuromorphic alternative by mimicking cortical architecture and biologicallyconstrained learning. They offer sparse architectures with local learning rules and unsupervised/semi-supervised learning, making them well-suited for low-power edge intelligence. However, existing BCPNN implementations rely on GPUs or datacenter FPGAs. This work presents the first embedded FPGA accelerator for BCPNN on a Zynq UltraScale+ SoC (ZCU104) using High-Level Synthesis. We implement both online learning and inference-only kernels with configurable precision (FP32, FP16, and mixed FP16/FXP16). Evaluated on MNIST, Pneumonia, and Breast Cancer datasets, our accelerator delivers up to 17.55% lower latency and 94.1% energy savings over ARM baselines. Our work brings practical, brain-like online learning and scalable inference to edge devices.

3. MCSoC 2025

   Evaluating Four FPGA-Accelerated Space Use Cases Based on Neural Network Algorithms for On-Board Inference

   Pedro Antunes, Muhammad Ihsan Al Hafiz, Jonah Ekelund, and 4 more authors

   In 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2025

   DOI
4. arXiv

   Efficient implementation of CRYSTALS-KYBER key encapsulation mechanism on ESP32

   Fabian Segatz and Muhammad Ihsan Al Hafiz

   arXiv preprint arXiv:2503.10207, 2025