Chronocam raises $15M in Series B; high-performance bio-inspired vision technology for autos and other machines
France-based Chronocam SA, a developer of biologically-inspired vision sensors and computer vision solutions for automotive, IoT and other applications requiring vision processing, raised $15 million in Series B financing. The funding comes from lead investor Intel Capital, along with iBionext, Robert Bosch Venture Capital GmbH, 360 Capital, CEAi and Renault Group.
Chronocam will use the investment to accelerate product development and commercialize its computer vision sensing and processing technology. The funding will also allow the company to expand into key markets, including the US and Asia.
Where traditional image sensors acquire static frame snapshots of an entire scene at fixed points in time, bio-inspired vision sensors sense the relevant dynamic scene context and acquire only what is necessary.
Chronocam’s proprietary approach leverages the company’s deep expertise in computer vision technology, sensor design and neuromorphic computing, which mimics the human brain. The Chronocam method uses innovative data capture techniques to greatly improve performance, dynamic range and power efficiency of cameras in a wide range of applications.
Chronocam’s sensors combine ultra-fast level-crossing sampling circuits with conditional integrative exposure measurements at the pixel level. Exposure measurements are performed pixel-individually and controlled by each pixel’s own sampling circuit.
Pixels individually and autonomously optimize the sampling of the image information based on the characteristics of the signal itself (amplitude-domain level-crossing sampling). Pixels sample fast if the light input changes fast and stop sampling if the input does not change.
The image information is encoded in the timing of asynchronous pulse signals autonomously generated and transmitted by each of the vpixels of the sensing array. In addition to sensor-level redundancy suppression / compressive sensing, time-domain encoding of the visual information optimizes the photo-charge integration, yielding improved signal-to-noise-ratio and ultra-wide dynamic range.
As a result, the sensors deliver high-speed, wide-dynamic-range full image and video data with ideal loss-less compression:
- Speed equivalent to 100,000 frames/second
- Dynamic range up to 140dB
- Video compression 10x – 1000x
The vision algorithms, based on biological information processing principles, are computationally efficient and highly parallelizable.
Conventional computer vision approaches are not well-suited to the requirements of a new generation of vision-enabled systems. For example, autonomous vehicles require faster sensing systems which can operate in a wider variety of ambient conditions. In the IoT segment, power budgets, bandwidth requirements and integration within sensor networks make today’s vision technologies impractical and ineffective.
Chronocam’s unique bio-inspired technology introduces a new paradigm in capturing and processing visual data, and addresses the most pressing market challenges head-on. We are well-positioned to capitalize on this significant market opportunity; and we appreciate the confidence demonstrated by our investors as we roll out our technology to an increasing number of customers.—Luca Verre, CEO and co-founder of Chronocam