Xmos provides a hardware‑agnostic software stack that abstracts CPUs, GPUs, FPGAs, and AI accelerators behind unified, language‑agnostic APIs. The platform automatically partitions and schedules workloads across heterogeneous resources, offering cross‑compilation, performance optimization, and real‑time monitoring to enable enterprises and developers to write code once and deploy it anywhere with low latency and high throughput.
Funding
$19M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CCFounders
Product
Problem
Enterprises and developers need a flexible, high‑performance compute platform that can run AI, data‑intensive, and real‑time workloads across heterogeneous hardware without extensive code rewrites or vendor lock‑in.
Solution
Xmos offers a hardware‑agnostic software stack that abstracts underlying processors, accelerators, and edge devices, enabling applications to be written once and deployed anywhere. The platform provides unified APIs, automated workload scheduling, and performance optimization tools that translate code to run efficiently on CPUs, GPUs, FPGAs, and specialized AI chips. By handling device heterogeneity and resource management, Xmos reduces development time and operational complexity while maintaining low latency and high throughput for demanding workloads.
Target Audience
Primary customers are enterprise software teams, AI developers, and system integrators that need to deploy compute‑intensive applications across data centers, edge locations, and mixed‑hardware environments.
Features
- Unified programming model with language‑agnostic APIs for AI, analytics, and real‑time processing
- Automatic workload partitioning and dynamic scheduling across heterogeneous compute resources
- Cross‑compilation toolchain that generates optimized binaries for CPUs, GPUs, FPGAs, and AI accelerators
- Real‑time monitoring dashboard with performance metrics, resource utilization, and predictive scaling recommendations
- Integration hooks for popular ML frameworks (TensorFlow, PyTorch) and data pipelines (Kafka, Spark)
- Security layer providing encrypted data flow and sandboxed execution across distributed nodes