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Implementation of artificial intelligence at the ‘edge’ (edge AI) is the use of AI techniques in products and devices at the edge of our connectivity networks, or even stand alone.

These applications, which often have severe power constraints because of their physical location, require vast amounts of local memory or a low-latency, high bandwidth connection to the cloud

The problem
with flash

Traditional architectures with non-volatile memory will use external Flash chips, but just like microcontrollers, they would significantly benefit from being able to integrate that memory on the same device as the processor

Fully integrated
memory

The ability to integrate the memory, which will have read access speed similar to the SRAM memory normally used, allows for new compute architectures with much higher memory bandwidth.

New AI architectures using Intrinsic RRAM therefore have the opportunity to be much higher performance and much lower power than those achievable with external non-volatile memory and internal SRAM

We get rid of the bottleneck

Intrinsic’s technology meets a pressing need. We can put efficient non-volatile memory directly on advanced processor chips, revolutionising next-generation microcontrollers and edge processors

Direct integration

Our technology exploits silicon dioxide – one of the most microelectronics-friendly materials there is. We use industry-standard fabrication and simple processes, and CMOS-compatibility is one of our core values and strengths

Low power consumption

Our silicon oxide devices consume orders of magnitude less power than Flash while offering superior performance

Low cost

Simple materials, simple design and CMOS compatibility make our technology low cost, yet high performance

Secure

The more computing power we can integrate into microcontrollers and edge processors, the less they need to communicate with the Cloud. That means less danger of data being intercepted