Key Takeaways & Executive Findings
- •• A monolithic spike-based hybrid-vision sensor integrates event detection and PFM circuits to output both temporal and texture spiking data. • Achieves 128×128 resolution, 960 Meps event rate, 117.1 kfps grayscale frame rate, and 60.1 mW power consumption in 180 nm CMOS. • Provides complete spike data streams compatible with synchronous SNN architectures, overcoming limitations of conventional EVS. • Suitable for high-speed, low-latency edge SNN-based vision computing systems.
Abstract
The event-based vision sensor (EVS), which can generate efficient spiking data streams by exclusively detecting motion, exemplifies neuromorphic vision methodologies. Generally, its inherent lack of texture features limits effectiveness in complex vision processing tasks, necessitating supplementary visual information. However, to date, no event-based hybrid vision solution has been developed that preserves the characteristics of complete spike data streams to support synchronous computation architectures based on spiking neural network (SNN). In this paper, we present a novel spike-based sensor with digitized pixels, which integrates the event detection structure with the pulse frequency modulation (PFM) circuit. This design enables the simultaneous output of spiking data that encodes both temporal changes and texture information. Fabricated in 180 nm process, the proposed sensor achieves a resolution of 128 × 128, a maximum event rate of 960 Meps, a grayscale frame rate of 117.1 kfps, and a measured power consumption of 60.1 mW, which is suited for high-speed, low-latency, edge SNN-based vision computing systems.
1. Introduction
Bio-inspired vision sensors mimic biological neural systems by representing temporal information through spiking signals, offering significant advantages in temporal resolution and dynamic range. A representative instance is the event-based vision sensor, which encodes temporal contrast exceeding a threshold and outputs in format of address-event representation (AER). Compared to frame-based image sensors, the event-based vision sensor (EVS) compresses redundant information and reduces readout operations with efficient data streams under low power consumption, suited for edge spike-neural-network (SNN) computing systems.
Generally, in many complex visual tasks, the dynamic data from EVS is insufficient to meet the requirements at the edge, and texture images from CMOS image sensors (CIS) are typically needed as a supplement to achieve optimal algorithm performance, such as image reconstruction, high-speed image deblur. While some studies have explored system-level integration of discrete CIS and EVS, this approach introduces challenges in image registration and synchronization, along with increased system size and power consumption.
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Huanhui Zhang, Chi Zhang, Xu Yang, Zhe Wang, Cong Shi, Runjiang Dou, Shuangming Yu, Jian Liu, Nanjian Wu, Peng Feng, Liyuan Liu (2025). A 128 × 128 monolithic spike-based hybrid-vision sensor with 0.96 Geps and 117 kfps. SinoTechIntel Verified Research. https://doi.org/10.1088/1674-4926/25020010
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Frequently Asked Questions
What is a spike-based hybrid-vision sensor?
It is a sensor that integrates event detection and pulse frequency modulation circuits to output spiking data encoding both temporal changes and texture information, supporting SNN-based vision systems.
What are the key performance metrics of the proposed sensor?
The sensor achieves 128×128 resolution, a maximum event rate of 960 Meps, a grayscale frame rate of 117.1 kfps, and power consumption of 60.1 mW.
How does this sensor differ from conventional event-based sensors?
Unlike conventional EVS that only output temporal contrast events, this sensor also provides texture information via PFM, producing complete spike data streams compatible with synchronous SNN architectures.
What are the potential applications of this sensor?
It is suited for high-speed, low-latency edge SNN-based vision computing systems, such as robotics, autonomous navigation, and real-time image processing.
What technology was used to fabricate the sensor?
The sensor was fabricated in a 180 nm CMOS process.
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