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.
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.
HQ
13 rue Jeanne Braconnier
Immeuble Le Pasteur
92360 Meudon-La-Forêt
France
Asia
Taipei
Taiwan
Japan
Tokyo
Japan
Korea
Seoul
Korea
USA
San Diego, CA
USA
Unmatched Performance at the Edge with Edge AI.
Fully programmable
Algorithm agnostic
Host processor agnostic
RISC-V core to offload & run AI completely on-chip
Tyr 4
fp8: 1600 Tflops
fp16: 400 Tflops
Tyr 2
fp8: 800 Tflops
fp16: 200 Tflops
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
Fully programmable
Algorithm agnostic
Host processor agnostic
RISC-V cores to offload host
& run AI completely on-chip.
fp8: 3200 Tflops
fp16: 800 Tflops
fp8/int8: 100 Tflops
fp16/int16: 50 Tflops
fp32/int32: 25 Tflops
Close to theory efficiency