# Programmable Silicon Retina on Pixel Processor Array

arXiv: https://arxiv.org/abs/2606.08370 · HTML: https://mlsanity.com/paper/2606.08370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision](https://mlsanity.com/paper/2404.19489/) (2024)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [CodedEvents: Optimal Point-Spread-Function Engineering for 3D-Tracking with Event Cameras](https://mlsanity.com/paper/2406.09409/) (2024)
- [PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors](https://mlsanity.com/paper/2304.05440/) (2023)
- [Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation](https://mlsanity.com/paper/2504.06748/) (2025)
- [Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging](https://mlsanity.com/paper/2211.09939/) (2022)
- [Bio-inspired visual attention for silicon retinas based on spiking neural networks applied to pattern classification](https://mlsanity.com/paper/2105.14753/) (2021)
- [Noise Filtering Benchmark for Neuromorphic Satellites Observations](https://mlsanity.com/paper/2411.11233/) (2024)
- [Neuromorphic Retina: An FPGA-based Emulator](https://mlsanity.com/paper/2501.08943/) (2025)

## Abstract (arXiv)

Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In this work, we explore whether incorporating additional biologically inspired processing stages - specifically spatial filtering and gain control - can offer advantages for certain downstream tasks such as saliency prediction. We present the first implementation of a multi-stage Silicon Retina model on the SCAMP-5 Pixel Processor Array, along with a GPU-based simulation framework. We evaluate the performance of our model on Video Intensity Reconstruction and Video Saliency Prediction. While the bio-inspired model is less effective at reconstructing absolute intensity frames, it achieves a 13% reduction in saliency prediction loss in comparison to standard DVS event representation, while reducing the event rate by approximately 47%. These experiments are obtained using a lightweight ≈ 100k-parameter FireNet-style network, adapted from event-based reconstruction to saliency prediction. These results suggest that the silicon retina's "information distillation" mechanism can achieve a more efficient representation for downstream neural networks, particularly in bandwidth-constrained edge applications.