BY PETE SINGER, Editor-in-Chief
Mukesh Khare, VP of IBM Research, talked about the impact artificial intelligence (AI) is going to have on the semiconductor industry during a recent panel session hosted by Applied Materials. He said that today most artificial intelligence is too complex. It requires, training, building models and then doing inferencing using those models. “The reason there is good in artificial intelligence is because of the exponential increase in data, and cheap compute. But, keep in mind that, the compute that we are using right now is the old compute. That compute was built to do spreadsheet, databases, the traditional compute.
“Since that compute is cheap and available, we are making use of it. Even with the cheap and available compute in cloud, it takes months to generate those models. So right now, most of the training is still being done in cloud. Whereas, inferencing, making use from that model is done at the edge. However, going forward, it is not possible because the devices at the edge are continuously generating so much data that you cannot send all the data back to the cloud, generate models, and come back on the edge.
“Eventually, a lot of training needs to move to the edge as well,” Khare said. This will require some innovation so that the compute, which is being done right now in cloud, can be transferred over to edge with low-power devices, cheap devices. Applied Materials’ CIO Jay Kerley added that innovation has to happen not only at the edge, but in the data center and at the network layer, as well as in the software frameworks. “Not only the AI frameworks, but what’s driving compression, de-duplication at the storage layer is absolutely critical as well,” he said.
Khare also weighed in on how transistors and memory will need to evolve to meet the demands of new AI computer architec- tures, “For artificial intelligence in our world, we have to think very differently. This is an inflection, but this is the kind of inflection that world has not seen for last 60 years.” He said the world has gone from tabulating system era (1900 to 1940) to the programmable system era in 1950s, which we are still using. “We are entering the era of what we call cognitive computing, which we believe started in 2011, when IBM first demonstrated artificial intelligence through our Watson System, which played Jeopardy,” he said.
Khare said “we are still using the technology of programmable systems, such as logic, memory, the traditional way of thinking, and applying it to AI, because that’s the best we’ve got.”
AI needs more innovation at all levels, Khare said. “You have to think about systems level optimization, chip design level optimization, device level optimization, and eventually materials level optimization,” he said. “The artificial workloads that are coming out are very different. They do not require the traditional way of thinking — they require the way the brain thinks. These are the brain inspired systems that will start to evolve.”
Khare believes analog compute might hold the answer. “Analog compute is where compute started many, many years ago. It was never adopted because the precision was not high enough, so there were a lot of errors. But the brain doesn’t think in 32 bits, our brain thinks analog, right? So we have to bring those technologies to the forefront,” he said. “In research at IBM we can see that there could be several orders of magnitude reduction in power, or improvement in efficiency that’s possible by intro- ducing some of those concepts, which are more brain inspired.”
Christos Georgiopoulos (former Intel VP and professor who was also on the panel) said a new compute model is required for A.I. “It’s important to understand that the traditional workloads that we all knew and loved for the last forty years, don’t apply with A.I. They are completely new workloads that require very different type of capabilities from the machines that you build,” he said. “With these new kind of workloads, you’re going to require not only new architectures, you’re going to require new system level design. And you’re going to require new capabilities like frameworks. He said TensorFlow, which is an open-source software library for machine intelligence originally developed by researchers and engineers working on the Google Brain Team, seems to be the biggest framework right now. “Google made it public for only one very good reason. The TPU that they have created runs TensorFlow better than any other hardware around. Well, guess what? If you write something on TensorFlow, you want to go to the Google backend to run it, because you know you’re going to get great results. These kind of architectures are getting created right now that we’re going to see a lot more of,” he said.