Applications

Making Dreams Come True

Application

Dogma - Particle Filter

To detect objects and the predict the individual object behaviour are the main elements of an autonomous vehicle’s detection system. As the name implies object detection is intended to localize and classify objects in the surrounding environment of the vehicle. Behaviour prediction is used to understand the dynamics of the objects in the surrounding environment and then to predict how they will behave in the future. This behaviour prediction is critical in the autonomous vehicle’s decision making and risk assessment. The quality of the autonomous vehicle behaviour is consequently directly related to how well these two stages can be done.

Application

Fusing Reasoning Models, Generative AI & LLMs: The Winning Formula

By combining structured, logic-driven reasoning engines with creative generative networks and large-scale language models, we unlock AI systems that are not only smarter but also more dependable. Here’s why this trio works so well together.

Explore Tyr

Unmatched Performance at the Edge with Edge AI.

Flexibility

Fully programmable

Algorithm agnostic

Host processor agnostic

RISC-V core to offload & run AI completely on-chip

Memory

Capacity

HBM: 36GB

Throughput

HBM: 1 TB/s

Performance

Tensorcore (dense)

Tyr 4
fp8: 1600 Tflops
fp16: 400 Tflops

Tyr 2
fp8: 800 Tflops
fp16: 200 Tflops

General Purpose

Tyr 4
fp8/int8: 50 Tflops
fp16/int16: 25 Tflops
fp32/int32: 12 Tflops

Tyr 2
fp8/int8: 25 Tflops
fp16/int16: 12 Tflops
fp32/int32: 6 Tflops

Close to theory efficiency

Flexibility

Fully programmable

Algorithm agnostic

Host processor agnostic

RISC-V cores to offload host
& run AI completely on-chip.

Memory

Capacity

HBM: 288GB

Throughput

HBM: 8 TB/s

Performance

Tensorcore (dense)

fp8: 3200 Tflops
fp16: 800 Tflops

General Purpose

fp8/int8: 100 Tflops
fp16/int16: 50 Tflops
fp32/int32: 25 Tflops

Close to theory efficiency

Explore Jotunn 8

Introducing the World’s Most Efficient AI Inference Chip.