Neural Network processor boosts performance of radar, lidar, vision applications

May 02, 2017 // By Christoph Hammerschmidt
It is generally accepted that automated driving requires huge computing capacities. What remains subject to discussion however is which processor architecture is best suited to run the object identification and sensor fusion algorithms that enable computers to drive vehicles. Now Cadence has, through its Tensilica division, introduced a processor based on a Digital Signal Processor (DSP) architecture. The Tensilica Vision C5 is designed to run all neural network layers of an AI engine.

One of the most widespread approaches to run AI systems is making use of Graphics Processor Units (GPUs). Their advantage: Standard graphics controllers for everyday computers typically contain many GPUs running in parallel. With the right algorithms, such graphic devices can be repurposed to AI engines that offer considerable performance. This is one of the recipes of success for market players like Nvidia. But not everyone believes that this approach is really the best one. “This approach requires very high-end GPUs that in turn consume vast amounts of energy,” comments Pulin Desai, product marketing director for the Tensilica Vision DSP product line. According to Desai, the GPU approach might be suitable for high-end server farms in environments where power is not critical. “In the car, you have to deal with embedded systems where space and power are limited.”


With the Tensilica Vision C5 DSP, Cadence suggests a different way. The C5 offers highly concentrated computing power – it offers a performance of 1 TeraMAC (Multiply-Accumulate computing steps) at the area of less than one square millimeter. This high performance is owed to its VLIW vector processing instruction set with 128-way, 8-bit or 64-way, 16-bit single-instruction multiple data (SIMD) execution. The device is optimized for vision, radar, lidar and sensor fusion applications with high availability requirements. And, important, it is architected for multi-processor designs – after all, state-of-the art ADAS, computer vision and other sensor information processing applications in vehicles are increasingly based on complex, heterogeneous multiprocessor designs.