Neuromorphic Processor with Universal Interconnections Implemented on FPGA Enables Real-Time Inference and Experimentation
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Neuromorphic computing, which draws inspiration from the human brain, offers a pathway to dramatically reduce energy consumption and accelerate real-time data processing. However, progress in this field requires accessible and adaptable hardware platforms, a need that Pracheta Harlikar, Abdel-Hameed A. Badawy, Prasanna Date, and colleagues at New Mexico State University and Oak Ridge National Laboratory now address. They have developed a low-cost neuromorphic processor built on a Xilinx FPGA, offering complete connectivity between artificial neurons and allowing researchers to precisely tailor neuron behaviour. This innovative design, validated using standard machine learning tasks such as image and data classification, demonstrates significant energy efficiency and scalability, and importantly, will be released as open-source software, promising to accelerate research into practical spiking neural networks. FPGA Platform for Spiking Neural Networks Researchers engineered a low-cost processor on a Xilinx Zynq-7000 FPGA platform to facilitate exploration of spiking neural networks, addressing a critical need for accessible, open-source neuromorphic hardware. The processor architecture supports all-to-all configurable connectivity, enabling flexible network topologies and complex interactions between simulated neurons, and implements the leaky integrate-and-fire (LIF) neuron model, a biologically plausible method for simulating neural activity. Crucially, the LIF model incorporates customizable parameters including threshold, synaptic weights, and refractory period, allowing researchers to precisely tune neuronal behavior and investigate the impact of these parameters on network performance. Communication between the processor and a host system occurs via a UART interface, a standard serial communication protocol, which enables runtime reconfiguration of the network without requiring hardware resynthesis, significantly accelerating the design and testing process.
The team validated the architecture’s functionality using benchmark datasets, including the Iris classification task and MNIST digit recognition, demonstrating its ability to perform complex pattern recognition tasks. The implementation will be released as open source to foster wider adoption and collaboration within the neuromorphic computing community. Neuromorphic Processor with Configurable Spiking Neurons Scientists have developed a low-cost, open-source neuromorphic processor implemented on a Xilinx Zynq-7000 FPGA platform, offering a flexible platform for spiking neural network research. The processor supports all-to-all configurable connectivity between neurons, enabling complex network architectures, and utilizes the leaky integrate-and-fire (LIF) neuron model with customizable parameters including threshold, synaptic weights, and refractory period. The LIF model accurately simulates biological neuron behavior, with the membrane potential evolving at each clock cycle based on weighted inputs, leakage, and thresholding. The architecture’s modular design allows for flexible parameter adjustment, supporting an 8-bit input width and adjustable number of inputs to suit diverse application requirements. Experiments demonstrate the system’s computational correctness and energy efficiency, validated using benchmark datasets including the Iris classification and MNIST digit recognition tasks. The system employs integer synaptic weights ranging from 0 to 255 and configurable delay values, allowing precise control over signal timing. The architecture employs the leaky integrate-and-fire neuron model, offering configurable parameters for threshold, synaptic weights, and refractory period, and supports all-to-all connectivity between neurons. Comprehensive testing using the Iris and MNIST datasets successfully classified all test cases, demonstrating the processor’s effectiveness and low resource usage.
The team validated the design’s scalability and energy efficiency, establishing a viable, adaptable platform for spiking neural network research and real-world applications. Communication with a host system occurs via a UART interface, allowing runtime reconfiguration without requiring hardware resynthesis, and the entire implementation will be released as open source. Future research directions include integrating higher-speed protocols such as Ethernet or USB, and exploring on-chip learning mechanisms like Spike-Timing-Dependent Plasticity, with the ultimate goal of comparing performance against conventional processors through ASIC implementation. 👉 More information 🗞 Neuromorphic Processor Employing FPGA Technology with Universal Interconnections 🧠 ArXiv: https://arxiv.org/abs/2512.10180 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Atomistic Study Reveals 2nm Thickness Limit for Non-Volatile Optical Properties of Monatomic Phase-Change Material December 13, 2025 Harnessing Vacuum Fluctuations Shapes Electronic and Photonic Behavior at the Micro- and Nanoscale December 13, 2025 Two-dimensional Helical Superconductivity and Gapless Edge Modes Emerge in 1T-WS/2H-WS Heterostructures December 13, 2025