Callosum provides a systems‑level platform that abstracts heterogeneous processors—including CPUs, GPUs, TPUs, FPGAs, and custom ASICs—into a unified resource pool with a common programming model and cross‑chip compiler. Its adaptive runtime scheduler and integrated auto‑tuning tools allocate AI workloads across the mixed‑technology fabric to optimize throughput, latency, and energy per inference, exposed through a cloud‑native API compatible with major ML frameworks.
Funding
Funding not disclosed
Founders
Product
Problem
Current AI scaling strategies depend on replicating identical processors and architectures, which constrains performance gains and cost efficiency when tackling problems that require diverse computational primitives. The ecosystem lacks a unified infrastructure to orchestrate heterogeneous silicon, forcing developers to stitch together ad‑hoc solutions that are fragile and hard to optimize.
Solution
Callosum delivers a systems‑level platform that enables heterogeneous chips and AI models to co‑evolve as a single intelligent system. The platform abstracts away low‑level hardware differences through a unified programming model, allowing researchers and chip vendors to compose compute pipelines from CPUs, GPUs, TPUs, FPGAs, and emerging ASICs. An adaptive runtime scheduler dynamically allocates workloads across the mixed‑technology fabric to maximize throughput and minimize energy per inference. Integrated profiling and auto‑tuning tools continuously optimize kernel placement and data movement, delivering order‑of‑magnitude improvements in capability‑to‑cost ratios. All components are exposed via a cloud‑native API, so teams can deploy, monitor, and iterate on heterogeneous AI solutions without rebuilding the stack for each new silicon generation.
Target Audience
Primary customers are large AI research labs, enterprise model developers, and semiconductor manufacturers that need to integrate multiple compute primitives into scalable, cost‑effective intelligent systems. Additionally, cloud service providers seeking to offer heterogeneous AI acceleration as a managed service are a key market segment.
Features
- Heterogeneous chip orchestration layer that abstracts CPUs, GPUs, TPUs, FPGAs, and custom ASICs into a single resource pool
- Unified programming model with language bindings for Python, C++, and Rust, enabling seamless model definition across diverse hardware
- Cross‑chip compiler that translates high‑level AI graphs into optimized kernels for each target architecture
- Adaptive runtime scheduler with latency‑aware load balancing and energy‑aware placement policies
- Integrated performance profiler and auto‑tuning engine that iteratively refines kernel parameters in production
- Secure, multi‑tenant API gateway with role‑based access control and end‑to‑end encryption for data and model artifacts
- Compatibility layer for major ML frameworks (TensorFlow, PyTorch, JAX) and standard container orchestration platforms (Kubernetes, Docker)
- Continuous research pipeline that publishes benchmark results and integrates frontier hardware innovations into the production stack