A chip up to 20x more efficient than the NVIDIA Jetson.

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.

A chip up to 20x more efficient than the NVIDIA Jetson.

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.

A chip up to 20x more efficient than the NVIDIA Jetson.

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.

Logic Circuits Optimized for GPT Architecture

Matrix–matrix multiplication, softmax, element-wise operations, and many other operators are executed with high efficiency.

Hardware Architecture

from GPT workloads

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.

Hardware logic circuits

Optimized Software
for this hardware

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.

Optimized Sofware

Hardware Architecture

Hardware architecture built to run GPT architecture almost at full utilization.

Low Latency

Custom hardware blocks for real-time performance.

Low Latency
Power Efficiency

Power Efficiency

No idle cycles or blocking operations.

Reconfigurable

Flexible

Modular compute instructions to implement any compute workloads.

Optimized Software

Mapping the compute chain with maximum efficiency to freshly generated hardware.

Advance Scheduling

We have efficient implementations for matrix-matrix multiplication, softmax and other critical operations to reach %90 or more utilization.

Optimized Sofware

Hardware-Oriented Software

Software is co-designed with hardware. Every instruction is scheduled with an awareness of logic placement, timing, and resources.

Hardware-Oriented Software

AI at the Edge Devices

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.