Workload intelligence
Profile the model at the operator, memory, precision, and dataflow level before committing to an architecture.
Develop the chip your AI was meant to run on.
AtlasLabs develops AI-ready ASICs around the models that matter: specialist systems for science, industry, and machines that cannot rely on general-purpose compute.
Start with the model. Build the silicon around its work.
General-purpose accelerators are designed for broad utilization. We develop ASICs for the moment a workload becomes valuable enough—and constrained enough—to deserve its own machine.
Domain experts, model researchers, compiler engineers, and hardware teams work from the same workload evidence. The model becomes the specification: its operators, memory movement, precision, latency, thermal limits, and failure modes.
Model–silicon co-design
Shape the model and accelerator together so each trade in accuracy, latency, power, and area is made deliberately.
ASIC development
Move from architecture study to an AI-ready ASIC envelope, compiler target, runtime, and production evidence.
The model changed the field. The machine should change with it.
Google DeepMind's AlphaFold showed what can happen when a model is designed around the structure of a scientific problem. Triformer applied the same specialist instinct to long-horizon multivariate forecasting, using triangular, variable-specific attention to improve efficiency.
AtlasLabs takes that logic into silicon. We study the architecture that makes a model exceptional, then develop the dataflow, memory hierarchy, numerical formats, compiler surface, and accelerator blocks that let it become a dependable machine.
Read the Triformer paperSilicon for fields where the model is becoming the instrument.
Four domains, each with different data, operators, tolerances, and physical limits. None should inherit its compute architecture by accident.
Biomedical
AI-ready silicon for protein models, genomics, medical imaging, and multimodal clinical inference—where throughput matters, but confidence and data locality matter more.
Aerospace
Deterministic, power-aware compute for autonomy, navigation, sensor fusion, and long-horizon telemetry at the edge, including environments where the cloud is not an option.
Language models
Purpose-built inference for private and embedded language systems, tuned around context, memory bandwidth, quantization, and the latency profile of the actual product.
Chemistry
Specialist acceleration for molecular property prediction, reaction modeling, materials search, and the graph and geometric workloads behind computational discovery.
A development loop shaped by the real world.
AI-ready ASICs get better when the workload, the model, and the hardware can answer each other before the expensive decisions harden.
Start with the constraint
Tell us where the system has to run, what it must know, and what failure costs. Atlas turns that into a model brief and an evaluation plan.
Explore the whole system
Researchers and hardware teams test architecture, data, precision, memory, and dataflow decisions against the same workload.
Ship what you measured
The result is a deployable model and runtime with traceable accuracy, latency, power, and cost—not a promising demo that changes later.
“The future of AI is not only a better model. It is a machine whose architecture carries the model's insight all the way into the physical world.”
A principle for every Atlas program
What should the system do?
Start with the task, the hardware, the data, or the constraint. Atlas will help shape the first build plan.