Dashcrystal provides a platform that compiles and optimizes machine‑learning models for deployment on custom silicon and FPGA accelerators, delivering low‑latency, power‑efficient inference. Its tools abstract hardware details, offering automated quantization, pruning, and performance profiling, plus an SDK and API for easy integration into edge devices, autonomous systems, and data‑center accelerators.
Funding
Funding not disclosed
Founders
Product
Problem
Deploying machine‑learning models at the edge often requires specialized hardware and complex software stacks, making it difficult for developers to achieve low‑latency inference on silicon or FPGA platforms. This limits the ability to run advanced AI workloads in power‑constrained or real‑time environments such as IoT devices, autonomous systems, and data‑center accelerators.
Solution
Dashcrystal offers a platform that streamlines the creation, optimization, and deployment of machine‑learning inference pipelines on custom silicon and FPGA hardware. The service abstracts hardware details, providing tools to compile models into high‑performance binaries that run directly on target accelerators with minimal latency. Integrated profiling and automated optimization ensure efficient resource utilization and power consumption. Users can manage deployments through a unified interface, enabling rapid iteration and scaling of AI workloads across diverse edge and data‑center use cases.
Target Audience
Primary customers are hardware engineers, AI developers, and system integrators building edge devices, autonomous platforms, or data‑center accelerators that require low‑latency, power‑efficient ML inference.
Features
- Model compiler that translates popular ML frameworks (TensorFlow, PyTorch) into FPGA‑compatible or ASIC‑ready inference binaries
- Automated hardware-aware optimization, including quantization, pruning, and pipeline parallelism
- Real‑time performance profiling and resource‑usage dashboards for latency, throughput, and power metrics
- SDK and API for seamless integration of compiled models into existing applications and firmware
- Support for a range of silicon and FPGA families, with customizable IP blocks for specialized workloads