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Video: How Intel will deliver on the Promise of AI

Over at the Intel Blog, Naveen Rao writes that this is an exciting week as we gather the brightest minds working with artificial intelligence (AI) at Intel AI DevCon, the company’s inaugural AI developer conference.

We recognize that achieving the full promise of AI isn’t something we at Intel can do alone. Rather, we need to address it together as an industry, inclusive of the developer community, academia, the software ecosystem and more. So as I take the stage today, I am excited to do it with so many others throughout the industry. This includes developers joining us for demonstrations, research and hands-on training. We’re also joined by supporters including Google*, AWS*, Microsoft*, Novartis* and C3 IoT*. It is this breadth of collaboration that will help us collectively empower the community to deliver the hardware and software needed to innovate faster and stay nimble on the many paths to AI.

Indeed, as I think about what will help us accelerate the transition to the AI-driven future of computing, it is ensuring we deliver solutions that are both comprehensive and enterprise-scale. This means solutions that offer the largest breadth of compute, with multiple architectures supporting milliwatts to kilowatts.

Enterprise-scale AI also means embracing and extending the tools, open frameworks and infrastructure the industry has already invested in to better enable researchers to perform tasks across the variety of AI workloads. For example, AI developers are increasingly interested in programming directly to open-source frameworks versus a specific product software platform, again allowing development to occur more quickly and efficiently.

Today, our announcements will span all of these areas, along with several new partnerships that will help developers and our customers reap the benefits of AI even faster.

Expanding the Intel AI Portfolio to Address the Diversity of AI Workloads

We’ve learned from a recent Intel survey that over 50 percent of our U.S. enterprise customers are turning to existing cloud-based solutions powered by Intel Xeon processors for their initial AI needs. This affirms Intel’s approach of offering a broad range of enterprise-scale products – including Intel Xeon processors, Intel Nervana and Intel Movidius technologies, and Intel FPGAs – to address the unique requirements of AI workloads.

One of the important updates we’re discussing today is optimizations to Intel Xeon Scalable processors. These optimizations deliver significant performance improvements on both training and inference as compared to previous generations, which is beneficial to the many companies that want to use existing infrastructure they already own to achieve the related TCO benefits along their first steps toward AI.

 

We are also providing several updates on our newest family of Intel Nervana Neural Network Processors (NNPs). The Intel Nervana NNP has an explicit design goal to achieve high compute utilization and support true model parallelism with multichip interconnects. Our industry talks a lot about maximum theoretical performance or TOP/s numbers; however, the reality is that much of that compute is meaningless unless the architecture has a memory subsystem capable of supporting high utilization of those compute elements. Additionally, much of the industry’s published performance data uses large square matrices that aren’t generally found in real-world neural networks.

At Intel, we have focused on creating a balanced architecture for neural networks that also includes high chip-to-chip bandwidth at low latency. Initial performance benchmarks on our NNP family show strong competitive results in both utilization and interconnect. Specifics include:

General Matrix to Matrix Multiplication (GEMM) operations using A(1536, 2048) and B(2048, 1536) matrix sizes have achieved more than 96.4 percent compute utilization on a single chip1. This represents around 38 TOP/s of actual (not theoretical) performance on a single chip1. Multichip distributed GEMM operations that support model parallel training are realizing nearly linear scaling and 96.2 percent scaling efficiency2 for A(6144, 2048) and B(2048, 1536) matrix sizes – enabling multiple NNPs to be connected together and freeing us from memory constraints of other architectures.

We are measuring 89.4 percent of unidirectional chip-to-chip efficiency3 of theoretical bandwidth at less than 790ns (nanoseconds) of latency and are excited to apply this to the 2.4Tb/s (terabits per second) of high bandwidth, low-latency interconnects.

All of this is happening within a single chip total power envelope of under 210 watts. And this is just the prototype of our Intel Nervana NNP (Lake Crest) from which we are gathering feedback from our early partners.

We are building toward the first commercial NNP product offering, the Intel Nervana NNP-L1000 (Spring Crest), in 2019. We anticipate the Intel Nervana NNP-L1000 to achieve 3-4 times the training performance of our first-generation Lake Crest product. We also will support bfloat16, a numerical format being adopted industrywide for neural networks, in the Intel Nervana NNP-L1000. Over time, Intel will be extending bfloat16 support across our AI product lines, including Intel Xeon processors and Intel FPGAs. This is part of a cohesive and comprehensive strategy to bring leading AI training capabilities to our silicon portfolio.

AI for the Real World

The breadth of our portfolio has made it easy for organizations of all sizes to start their AI journey with Intel. For example, Intel is collaborating with Novartis on the use of deep neural networks to accelerate high content screening – a key element of early drug discovery. The collaboration team cut time to train image analysis models from 11 hours to 31 minutes – an improvement of greater than 20 times4.

To accelerate customer success with AI and IoT application development, Intel and C3 IoT announced a collaboration featuring an optimized AI software and hardware solution: a C3 IoT AI Appliance powered by Intel AI.

Additionally, we are working to integrate deep learning frameworks including TensorFlow*, MXNet*, Paddle Paddle*, CNTK* and ONNX* onto nGraph, a framework-neutral deep neural network (DNN) model compiler. And we’ve announced that our Intel AI Lab is open-sourcing the Natural Language Processing Library for JavaScript* that helps researchers begin their own work on NLP algorithms.

The future of computing hinges on our collective ability to deliver the solutions – the enterprise-scale solutions – that organizations can use to harness the full power of AI. We’re eager to engage with the community and our customers alike to develop and deploy this transformational technology, and we look forward to an incredible experience here at AI DevCon.

Naveen G. Rao is corporate vice president and general manager of the Artificial Intelligence Products Group at Intel Corporation. Rao’s team focuses on deep learning, a subset of machine learning and artificial intelligence, and works to develop the hardware and software ingredients needed to enable its scalable deployment. Intel uses deep learning to accelerate complex, data-intensive processes, such as image recognition and natural language processing, to improve the performance of Intel Xeon and Intel Xeon Phi processors in various business segments, including autonomous driving and personalized medicine.

Trained as both a computer architect and neuroscientist, Rao joined Intel in 2016 with the acquisition of Nervana Systems. As chief executive officer and co-founder of Nervana, he led the company to become a recognized leader in the deep learning field. Before founding Nervana in 2014, Rao was a neuromorphic machines researcher at Qualcomm Inc., where he focused on neural computation and learning in artificial systems. Rao’s earlier career included engineering roles at Kealia Inc., CALY Networks and Sun Microsystems Inc.

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