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Optimising SoCs with deep learning

Posted: 01 Apr 2015     Print Version  Bookmark and Share

Keywords:deep learning  CNN  Opus  Zeroth  neural networks 

In the deep architecture, however, you can integrate all the steps into one, he explained. "You need to make no decision, because deep learning will make decisions for you."

In other words, as Bier summed up: "Traditional computer vision took a very procedural approach in detecting objects." Deep learning is, however, a radical departure, he said, because "you don't have to tell computers where to look."

Bier described the process as a two-phase approach. Learning and training done at dedicated facilities, such as data centres, by using super computers. Then, large data sets in the first phase are translated into "settings" and "co-efficient" for embedded systems to use, said Bier.

Computer vision expert Fei-Fei Li discusses how we are teaching computers to understand pictures.

SoCs optimised for neural networks?

No consensus appears to have emerged in terms of the best architecture for CNN in embedded Vision SoCs.

Cognivue and the University of Ottawa's Laganière believe that a massively parallel architecture is the way for efficiently processing a convolutional neural network. In parallel processing, an image to which certain parameters are applied produces another image, and as another filter is applied to the image, it produces another image. "So you may need more internal local memory to store intermediate results in SoCs," said Laganière.

The bad news is that in a big CNN, you could end up with billions of parameters. "But the good news is that there are tricks that we can use to simplify the process and remove some connections that are not needed," he explained. The challenge, however, remains in handling a number of different nodes in CNN, and you can't predetermine which node needs to be connected to another node. "That's why you need a programmable architecture. You can't hardwire the connections," said Laganière.

Meanwhile, Bier said that in designing a processor for CNN, "You could use a simple, uniform architecture." Rather than designing a different SoC architecture or optimising it every time new algorithms pop up, a CNN processor only needs a "fairly simple algorithm that comes with fewer variables," he explained. In other words, "One could even argue that you can reduce programmability for a neural network processor" if we know the right settings and co-efficient to be fed. "But many companies aren't ready to make that bet yet, because things are still developing," added Bier.

Chip vendors are using everything from CPU and GPU to FPGA and DSP to enable CNN on vision SoCs. So the debate over CNN architecture has only begun, in Bier's opinion.

While there is no question that deep learning is altering the future of embedded-vision SoC designs, Bier said that a leading vision chip company like Mobileye has accumulated substantial vision-based automotive safety expertise. "I know many rivals want to eat their lunch, but I think an incumbent like Mobileye still has the first mover advantage."

Baidu's Wu, asked about the challenges of deep learning in smartphones and wearable devices, pointed out three. First, "We are still looking for a killer app," he said. When the industry developed an MP3 player, for example, people knew exactly what it was for. This made it easy to develop a necessary SoC. While on-device deep learning sounds cool, what is its best application? No one knows yet, according to Wu.

Second, "Deep learning needs an ecosystem," he said. Collaboration among research institutes and companies is critical and "very useful," he said.

Third, "We want to make smaller devices capable of deep learning," said Wu. "Making it high performance at lower power will be the key."

The topic of bringing deep learning to embedded systems is close to Wu's heart. He will be a keynote speaker at the Embedded Vision Summit on May 12 in Santa Clara. He'll speak about "Enabling Ubiquitous Visual Intelligence Through Deep Learning."

- Junko Yoshida
  EE Times

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