Darkgrade offers a hardware‑agnostic binary protocol and cross‑platform SDKs that let large language models consume raw image sensor data directly, bypassing traditional ISP processing. This reduces latency and bandwidth for edge vision applications in robotics, autonomous vehicles, and other low‑power systems, while providing secure, encrypted communication and compatibility with major AI frameworks.
Funding
Funding not disclosed
Founders
Product
Problem
Current vision pipelines require image data to be captured, encoded, and transmitted to separate compute units before large language models (LLMs) can analyze the content. This multi‑stage flow adds latency, increases bandwidth consumption, and limits the feasibility of real‑time edge inference for robotics, autonomous vehicles, and other low‑power vision systems.
Solution
Darkgrade provides an open, hardware‑agnostic protocol that lets LLMs communicate directly with image sensor ASICs, bypassing traditional image‑signal‑processor (ISP) stages. The protocol defines a binary interface and a set of SDKs that expose raw sensor readouts as token streams consumable by LLM inference engines. By moving inference onto the sensor, the solution reduces end‑to‑end latency, cuts data movement overhead, and enables on‑device decision making with sub‑millisecond response times. Compatibility layers map the protocol to major LLM frameworks (e.g., PyTorch, TensorFlow) and to standard sensor drivers, allowing OEMs and AI developers to integrate the stack without redesigning hardware. All interactions are secured with mutual authentication and encrypted transport, ensuring data integrity in safety‑critical deployments.
Target Audience
Primary customers are camera module manufacturers, robotics OEMs, and autonomous‑vehicle platform providers that require ultra‑low‑latency vision processing, as well as AI software firms building edge‑vision applications for drones, industrial automation, and smart sensors.
Features
- Binary protocol specification for direct LLM‑to‑sensor communication, supporting RAW Bayer and CMOS data formats
- Cross‑platform SDKs (C++, Rust, Python) that abstract sensor registers and expose a token‑based API for LLM inference
- Hardware abstraction layer (HAL) that auto‑detects sensor capabilities (frame rate, exposure, HDR) and negotiates optimal data streams
- Integrated mutual TLS authentication and AES‑256 payload encryption for secure on‑sensor inference pipelines
- Compatibility adapters for PyTorch, TensorFlow, and ONNX Runtime, enabling drop‑in replacement of conventional image preprocessing modules
- Edge‑deployment toolkit with containerized runtime, profiling utilities, and latency benchmarking scripts
- Open‑source reference implementation and comprehensive documentation hosted on GitHub, with versioned releases and CI‑validated test suites