[Paper] Neuromorphic FPGA Design for Digital Signal Processing

Source: arXiv - 2601.07069v1

Overview

Justin London’s paper explores how neuromorphic concepts—spiking neural networks (SNNs) and memristor‑based synapses—can be folded into FPGA designs for classic digital‑signal‑processing (DSP) blocks such as FIR and IIR filters. By prototyping both conventional and neuromorphic‑enhanced filters in Verilog on Xilinx Vivado, the work shows that event‑driven, on‑chip learning can cut latency and power, albeit with a trade‑off in numeric precision.

Key Contributions

• Neuromorphic‑augmented FIR/IIR filter architectures implemented on a commercial FPGA fabric.
• Verilog HDL reference designs for both standard and spiking‑neuron‑based filter implementations, publicly reproducible in Vivado.
• Quantitative comparison of latency, power draw, and resource utilization between traditional DSP blocks and their neuromorphic counterparts.
• Demonstration of on‑chip synaptic plasticity (online weight adaptation) without external host intervention, highlighting continuous learning capability.
• Analysis of precision vs. efficiency trade‑offs, providing concrete numbers on how reduced bit‑width in SNNs impacts filter quality.

Methodology

1. Background synthesis – The paper first reviews SNN fundamentals (leaky‑integrate‑and‑fire neurons, spike timing dependent plasticity) and memristor models that emulate synaptic weight updates.
2. Design mapping – Classical FIR/IIR filter equations are reformulated as spike‑based convolution operations. Each filter tap becomes a synapse whose weight is stored in a memristor‑like register.
3. Hardware implementation – Two Verilog modules are written:
   • Baseline: classic fixed‑point FIR/IIR using Xilinx DSP slices.
   • Neuromorphic: event‑driven SNN filter where input samples generate spikes, neurons accumulate weighted spikes, and plasticity rules adjust weights on the fly.
4. Simulation & synthesis – Both designs are simulated with test vectors (sinusoidal, noisy, and step inputs) and synthesized for a Xilinx Artix‑7 device. Power and timing reports are extracted from Vivado’s analysis tools.
5. Evaluation metrics – Latency (clock cycles per output sample), dynamic power (mW), LUT/FF/DSP usage, and output signal‑to‑noise ratio (SNR) are recorded for each configuration.

Results & Findings

• Latency drops dramatically because spikes trigger computation only when activity occurs, eliminating idle cycles.
• Power savings stem from the event‑driven nature and the removal of DSP slice toggling.
• Resource footprint is smaller, freeing up fabric for additional logic or parallel filters.
• Precision suffers a ~10 dB SNR penalty, reflecting the coarse granularity of spike‑based encoding.
• Learning: the neuromorphic filters continuously adjust tap weights in response to input statistics, something the static baseline cannot do without a host‑side re‑programming step.

Practical Implications

• Edge‑AI & IoT – Low‑power, low‑latency filtering directly on an FPGA can preprocess sensor streams (audio, vibration, RF) before feeding them to a downstream neural network, extending battery life.
• Adaptive communications – Real‑time equalization or echo cancellation could benefit from on‑chip learning, allowing the filter to track channel drift without firmware updates.
• Rapid prototyping – The Verilog reference designs give hardware engineers a ready‑made template to experiment with spiking‑based DSP blocks in existing FPGA toolchains.
• Hybrid architectures – Teams can mix conventional DSP slices for high‑precision paths and neuromorphic blocks for coarse, adaptive preprocessing, achieving a balanced power‑performance trade‑off.
• Reduced von Neumann bottleneck – By keeping weight updates on the same fabric that performs convolution, data movement is minimized—critical for latency‑sensitive applications like autonomous robotics.

Limitations & Future Work

• Numeric precision – The 8‑bit spike representation limits filter fidelity, making the approach unsuitable for high‑dynamic‑range audio or RF front‑ends without additional compensation.
• Memristor abstraction – The study models memristive behavior in RTL; real hardware memristors may exhibit variability, endurance, and non‑idealities not captured in simulation.
• Scalability – Experiments were limited to modest tap counts (≤32). Scaling to large‑order filters may re‑introduce resource pressure and require hierarchical spike routing.
• Learning rule exploration – Only a basic STDP rule was used. Future work could investigate more sophisticated plasticity mechanisms (e.g., reinforcement‑driven updates) and their impact on filter convergence.
• Toolchain support – Current FPGA synthesis tools lack native awareness of spiking semantics, so designers must manually map SNN behavior to RTL. Integrating neuromorphic primitives into high‑level synthesis (HLS) could streamline development.

Bottom line: London’s work demonstrates that neuromorphic computing isn’t just a brain‑inspired curiosity—it can be a pragmatic way to shave latency and power from everyday DSP tasks on FPGAs, provided developers are comfortable trading a bit of precision for adaptive, event‑driven efficiency.

Authors

• Justin London

Paper Information

• arXiv ID: 2601.07069v1
• Categories: cs.NE, eess.SP
• Published: January 11, 2026
• PDF: Download PDF