We designed a compute architecture from scratch that combines systolic arrays and vector processing in a unified engine with multiple architectural optimizations. The result is extremely high hardware utilization and be one of the smallest logic-footprint compute engines developed so far delivering up to 20x more efficient computation.
Matrix–matrix multiplication, softmax, element-wise operations, and many other operators are executed with high efficiency.
Our architecture is designed specifically for GPT workloads. By analyzing the full attention kernel including matrix multiplication, quantization, normalization and data movement we derived a hardware architecture optimized for the most critical operations. The design combines systolic arrays and vector processing units with optimized data paths to maximize utilization and minimize memory movement overhead.
Our software stack analyzes the compute graph and maps operations efficiently onto the hardware architecture. Operators are carefully scheduled to maximize utilization of matrix multiplication, quantization, and data movement units while maintaining general compute support for evolving workloads.
Hardware architecture built to run GPT architecture almost at full utilization.
Custom hardware blocks for real-time performance.
No idle cycles or blocking operations.
Modular compute instructions to implement any compute workloads.
Mapping the compute chain with maximum efficiency to freshly generated hardware.
We have efficient implementations for matrix-matrix multiplication, softmax and other critical operations to reach %90 or more utilization.
Software is co-designed with hardware. Every instruction is scheduled with an awareness of logic placement, timing, and resources.
We enable complex AI models—LLMs, vision transformers, and VLA architectures—to run efficiently on edge devices. Our compute design delivers high performance with low power usage, while keeping data local for maximum privacy and responsiveness.
Modern drones demand real-time decision-making on the edge. By running advanced vision models and navigation algorithms locally—with low power and zero cloud dependency—we unlock faster response times, greater autonomy, and enhanced privacy in the air.With their autonomous flight capability, AI-powered data analysis and wide range of applications, drones are redefining the way industries work.
From perception to planning, autonomous vehicles require high-throughput AI at ultra-low latency. Our architecture enables complex models like vision transformers and sensor fusion networks to run locally—boosting safety, reliability, and performance without relying on the cloud.Equipped with sensors, machine learning and advanced navigation systems, autonomous vehicles are making the future of transportation safe, efficient and sustainable.
Robots need to perceive, decide, and act—all in real time. By running AI models directly on-device, from object detection to motion planning, we enable responsive, power-efficient robotics that operate reliably in dynamic environments without cloud latency.Advancing human-machine collaboration, robotic technologies are taking efficiency and automation to a new level in many areas from production to health.
Our architecture powers AI without the cloud—keeping data local, secure, and fast. From enterprise systems to remote edge devices, we enable complex models to run independently, reducing latency, preserving privacy, and cutting infrastructure costs.Centered on data security and independent processing power, off-cloud solutions offer low-latency, high-performance computing infrastructures for critical systems.
