Frequently Asked Questions
Find answers to common questions about Branes.AI and our Design Explorer.
How does the KPU handle sparse data and non-linear signal processing compared to a standard GPU?
Unlike GPUs, which rely on massive parallelization of dense matrices (optimized for graphics and LLM training), the KPU architecture is designed for the high-velocity, sparse data environments typical of Embodied AI. It features a specialized “Perception-to-Action” pipeline that integrates signal processing, DNN acceleration, and non-linear filtering into a single silicon fabric. This eliminates the PCIe and memory-bus latency bottlenecks found in CPU+GPU setups, enabling sub-millisecond response times for complex autonomy tasks.
Does BRANES AI support standard ML frameworks like PyTorch or JAX, or does it require a proprietary language?
We prioritize developer velocity. The BRANES SDK provides a “one-click” compilation path for models built in PyTorch, TensorFlow, and JAX. Our compiler automatically maps standard operators to KPU-native kernels. For advanced users, we offer a low-level API for custom kernel development, ensuring that our “Fully Programmable” promise remains accessible without forcing a departure from your existing software stack
You mention a range from 1W to 100W. Is this the same chip, or a family of products?
BRANES AI utilizes a modular, tiled architecture. Our silicon can be scaled from a “Single-Core” configuration (1W–5W) for edge sensors and wearables to a “Multi-Tile” array (up to 100W) for high-performance mobile robotics and autonomous vehicles. This allows customers to maintain a single software codebase across their entire product line, from low-power monitoring to high-compute navigation.
Why focus exclusively on Embodied AI instead of the booming Datacenter/LLM market?
The datacenter market is a “battle of the giants” focused on massive-batch training. However, the next frontier of AI is physical. Embodied AI (robotics, drones, vehicles) requires a completely different compute profile: low batch sizes (typically Batch-1), extreme energy constraints, and real-time deterministic latency. By specializing in the “Sense-Act” loop rather than “Input-Text” loops, we provide 20x higher efficiency for the hardware that will actually power the physical world.
How does the KPU design address the "Silicon Tax" and cost-per-object tracked?
General-purpose chips (CPUs/GPUs) waste a significant percentage of their silicon area on features—like cache coherency for thousands of threads or legacy instruction sets—that Embodied AI simply doesn’t use. Our KPU strips away this “silicon tax,” dedicating every square millimeter to the mathematical kernels relevant to perception and autonomy. This results in a 10x increase in compute density, allowing us to deliver 5x more tracking capacity per dollar of hardware.
What is Branes.AI’s Design Explorer?
Our patented platform delivers guaranteed software-hardware co-design recommendations in 24 minutes, optimizing energy, performance, and cost.
How does Branes.AI compare to other tools?
Inspired by tools like Google’s Model Explorer, Branes.AI uniquely focuses on holistic co-design, bridging software and hardware for embodied AI.
Who can benefit from Branes.AI?
Startups, enterprises, and research institutions developing robotics, automotive, or industrial AI systems can accelerate their design process.
What hardware platforms do you support?
We support COTS (CPU, GPU, NPU), FPGA, and ASIC configurations, tailored to your project’s needs.
Technical FAQs
Dive deeper into the technical aspects of our platform.
How does the Design Explorer work?
It uses the IREE/MLIR framework to simulate configurations, producing Pareto graphs for data-driven decisions.
Can I customize EPC metrics?
Yes, our platform allows you to prioritize energy, performance, or cost based on your project requirements.
What environments do you support?
From 100 sq ft vehicles to 50,000 sq ft industrial floors, our solutions are scalable and adaptable.