# OpenPanel

OpenPanel is a French startup developing AI-optimized computer vision chips, focusing on energy-efficient hardware for edge and data center applications. Founded in the late 2010s, it targets high-performance inference for neural networks.

OpenPanel is a French startup specializing in the design of application-specific integrated circuits (ASICs) optimized for [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) workloads, particularly [computer-vision](https://www.wikiprompt.org/wiki/computer-vision) tasks. The company develops hardware accelerators that aim to deliver high inference throughput while minimizing power consumption, positioning itself within the competitive landscape of AI chipmakers that includes established players like [nvidia](https://www.wikiprompt.org/wiki/nvidia) and emerging startups such as [cerebras](https://www.wikiprompt.org/wiki/cerebras) and [groq](https://www.wikiprompt.org/wiki/groq). OpenPanel's technology targets both edge devices and data center deployments, addressing the growing demand for efficient processing of [neural-network](https://www.wikiprompt.org/wiki/neural-network) models.

Founded in the late 2010s by a team of engineers and researchers with backgrounds in semiconductor design and machine learning, OpenPanel has its headquarters in Paris, France. The startup has attracted attention for its novel chip architecture, which integrates memory and compute units to reduce data movement bottlenecks, a key challenge in [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) inference. As of 2024, OpenPanel has raised several funding rounds from European venture capital firms and strategic investors, though specific financial details remain private.

## Chip Architecture

OpenPanel's core product is a family of AI accelerators built around a dataflow architecture that maps [neural-network](https://www.wikiprompt.org/wiki/neural-network) layers directly onto hardware. Unlike traditional GPUs, which rely on massive parallel processing with external memory, OpenPanel chips employ a systolic array design with on-chip SRAM, enabling higher energy efficiency. This approach is similar in principle to [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind)'s TPU designs but tailored for vision models, which often involve convolutional operations with high spatial locality.

The chips support a range of precisions, including INT8 and FP16, with a roadmap for lower-bit formats like INT4 to further boost throughput. Benchmark results published by the company claim up to 10 tera-operations per second per watt, a figure that would place it ahead of many commercial offerings, though independent verification is pending. The architecture also includes a custom compiler that translates models from frameworks like TensorFlow and PyTorch into optimized instructions, easing integration for developers.

## Target Applications

OpenPanel focuses on computer vision applications across several verticals. In the automotive sector, the chips are designed for advanced driver-assistance systems (ADAS) and autonomous driving, competing with solutions from [tesla-autopilot](https://www.wikiprompt.org/wiki/tesla-autopilot) and [waymo](https://www.wikiprompt.org/wiki/waymo). The low-latency and power-efficient characteristics make them suitable for real-time object detection and semantic segmentation in vehicles.

In industrial automation, OpenPanel partners with manufacturers to deploy vision-based quality inspection systems. The hardware can process high-resolution images at speeds exceeding 60 frames per second, enabling inline defect detection. Additionally, the startup targets smart city applications, such as traffic monitoring and surveillance, where edge processing reduces the need for cloud connectivity and enhances data privacy.

## Software Ecosystem

To complement its hardware, OpenPanel has developed a software stack that includes a runtime library, model optimization tools, and a simulation environment. The compiler, named OpenFlow, automatically applies techniques like layer fusion, pruning, and quantization to reduce model size and latency. This toolchain supports popular [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) frameworks and integrates with onnx-runtime for cross-platform deployment.

The company also offers a cloud-based development platform where users can prototype and benchmark models on simulated hardware before purchasing physical chips. This approach lowers the barrier for adoption, particularly for small and medium enterprises without in-house hardware expertise. OpenPanel maintains an open-source repository for its runtime, fostering a community of developers who contribute to performance tuning and bug fixes.

## Competitive Landscape

The AI chip market is highly competitive, with incumbents like [intel](https://www.wikiprompt.org/wiki/intel) and [amd](https://www.wikiprompt.org/wiki/amd) investing heavily in accelerators, alongside specialized startups such as [samba-nova](https://www.wikiprompt.org/wiki/samba-nova) and [graphcore](https://www.wikiprompt.org/wiki/graphcore). OpenPanel differentiates itself through a focus on vision-specific workloads, whereas many rivals target general-purpose [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) inference. This niche allows the company to optimize its silicon for convolutional and transformer-based vision architectures, potentially achieving better performance-per-dollar for these tasks.

Compared to [cerebras](https://www.wikiprompt.org/wiki/cerebras)' wafer-scale engines, OpenPanel's chips are smaller and more cost-effective for edge deployments. Against [groq](https://www.wikiprompt.org/wiki/groq), which emphasizes ultra-low latency for language models, OpenPanel prioritizes energy efficiency for continuous vision processing. The startup also faces competition from [qualcomm](https://www.wikiprompt.org/wiki/qualcomm) and [arm-holdings](https://www.wikiprompt.org/wiki/arm-holdings) in the mobile and embedded segments, though OpenPanel's custom design offers more flexibility for specialized use cases.

## Funding and Growth

OpenPanel was founded in 2018 by Antoine Chevrot, a former chip architect at [broadcom](https://www.wikiprompt.org/wiki/broadcom), and Marie Dupont, a machine learning researcher from [mit-csail](https://www.wikiprompt.org/wiki/mit-csail). The company initially operated in stealth mode, securing seed funding from Paris-based accelerator programs. In 2021, it closed a Series A round of €15 million led by a consortium of European investors, including the French public investment bank Bpifrance.

A subsequent Series B in 2023 raised €40 million, with participation from strategic partners in the automotive and industrial sectors. These funds have been allocated to expanding the engineering team, developing the second-generation chip, and establishing a sales presence in North America and Asia. As of early 2025, OpenPanel employs approximately 120 people, with offices in Paris and Grenoble, a hub for semiconductor research in France.

## Manufacturing and Partnerships

OpenPanel operates as a fabless semiconductor company, partnering with [tsmc](https://www.wikiprompt.org/wiki/tsmc) for manufacturing its chips using advanced process nodes, including 7nm and 5nm technologies. This partnership ensures access to state-of-the-art fabrication capabilities without the capital expenditure of owning a fab. The company also collaborates with [samsung-electronics](https://www.wikiprompt.org/wiki/samsung-electronics) for packaging and testing services, leveraging their expertise in high-density interconnects.

In terms of ecosystem partnerships, OpenPanel has joined the [arm-holdings](https://www.wikiprompt.org/wiki/arm-holdings) Arm Flexible Access program to license CPU cores for its system-on-chip designs, integrating general-purpose processing with its AI accelerators. The startup is also a member of the MLCommons consortium, contributing to benchmarking standards for AI hardware. These collaborations enhance credibility and facilitate adoption among enterprise customers.

## Research and Development

OpenPanel maintains an active research division that publishes papers at major conferences like NeurIPS and CVPR. Recent work focuses on sparsity-aware computing, where the hardware dynamically skips zero-valued activations to improve efficiency. This research is informed by collaborations with academic institutions, including [university-of-toronto](https://www.wikiprompt.org/wiki/university-of-toronto) and [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), where the company sponsors doctoral fellowships.

The R&D team also explores novel memory technologies, such as compute-in-memory using resistive RAM, which could further reduce energy consumption in future generations. As of 2025, these technologies are in early prototyping stages, with commercial deployment expected within three to five years. The company holds several patents related to dataflow scheduling and on-chip interconnect design, strengthening its intellectual property portfolio.

## Future Outlook

OpenPanel aims to release its second-generation chip, codenamed 'VisionX', in late 2025, featuring a 3nm process and support for [transformer](https://www.wikiprompt.org/wiki/transformer)-based vision models like ViT. The company plans to expand into the medical imaging market, partnering with firms like [intuitive-surgical](https://www.wikiprompt.org/wiki/intuitive-surgical) for real-time surgical guidance. Additionally, OpenPanel is exploring opportunities in the defense sector, though such engagements are subject to regulatory approvals.

The startup faces challenges in scaling production and competing with well-funded rivals, but its specialized focus and energy-efficient designs position it well for the growing edge AI market. As demand for on-device intelligence rises, OpenPanel's technology could play a significant role in enabling next-generation vision applications across industries.

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Source: https://www.wikiprompt.org/wiki/open-panel
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-05T13:21:31.741355+00:00