Keynotes

Industrial IoT (IIoT) Systems are now becoming a reality. IIoT is distributed by nature, encompassing many complementary technologies. IIOT systems are composed of sensors, actuators, a means of communication and control units, and are moving into the factories, with the Industry 4.0 generation. In order to operate concurrently, all these IIoT components will require a wide range of technologies, in order to maintain such system-of-systems in a full operational, coherent and secure state. We identify and describe the four key enablers for the Industrial IoT:

1. more powerful and diverse embedded computing, available on ST’s latest STM32 microcontrollers and microprocessors,
2. augmented by AI applications at the edge ( in the end devices), whose development is becoming enormously simplified by our specialized tools,
3. a wide set of connectivity technology, either with complete System-on-chip, or ready-to-use modules, and
4. a scalable security offer, thanks to either integrated features or dedicated security devices.

We conclude with some perspective on the usage of Digital Twins in the IIoT.

Edge Artificial Intelligence is the new megatrend, as privacy concerns and networks bandwidth/latency bottlenecks prevent cloud offloading of sensor analytics functions in many application domains, from autonomous driving to advanced prosthetic. The next wave of "Extreme Edge AI"  pushes aggressively towards sensors and actuators, opening major research and business development opportunities.  In this talk I will give an overview of recent efforts in developing an Extreme Edge AI platform based on open source parallel ultra-low power (PULP) Risc-V processors and accelerators. I will then look at what comes next in this brave new world of hardware reinaissance.

Though neuromorphic systems were introduced decades ago, there has been a resurgence of interest in recent years due to the looming end of Moore's law, the end of Dennard scaling, and the tremendous success of AI and deep learning for a wide variety of applications.  With this renewed interest, there is a diverse set of research ongoing in neuromorphic computing, ranging from novel hardware implementations, device and materials to the development of new training and learning algorithms. There are many potential advantages to neuromorphic systems that make them attractive in today’s computing landscape, including the potential for very low power, efficient hardware that can perform neural network computation.  Though some compelling results have been demonstrated thus far that demonstrate these advantages, there is still significant opportunity for innovations in hardware, algorithms, and applications in neuromorphic computing.  In this talk, a brief overview of the history of neuromorphic computing will be discussed, and a summary of the current state of research in the field will be presented.  Finally, a list of key challenges, open questions, and opportunities for future research in neuromorphic computing will be enumerated. 

The buzz about AI is deafening. Compelling applications are starting to emerge, dramatically changing the customer service that we experience, the marketing messages that we receive, and some systems we use. But, as organizations decide whether and how to incorporate AI in their systems and services, they must bring together new combinations of specialized knowledge, domain expertise, and business objectives. They must navigate through numerous choices – algorithms, processors, compute placement, data availability, architectural allocation, communications, and more. At the same time, they must keep their focus on the applications that will create compelling value for them. In this keynote, Jim Tung looks at the promising opportunities and practical challenges of building AI into our systems and services.

The Cloud is promising orders of magnitude savings in time to market for integrated circuits, owing to CPU elasticity. However, practical limitations still mandate a selective approach to the product design flow and the business models have yet to be fully defined by vendors. The economic equation of designing in the cloud is challenging. In addition, design houses, IDMs and OEM customers have to decide to what extent they want to rely on Cloud service providers to maintain the confidentiality of their IP or SOC databases and honor export control requirements, in a context where such concerns are increasingly relevant in EDA vendor and IC supplier selection. This keynote will explore those topics based on ST’s own experience and trials.

As any other area of our lives, medicine is experiencing the digital revolution. We produce more and more quantitative data in medicine, and therefore, we need significantly more compute power and data storage capabilities in the near future. Yet, since medicine is inherently decentralized, current compute infrastructures are not build for that. Central cloud storage and centralized super computing infrastructures are not helpful in a discipline such as medicine that will produce data always at the edge. Here we completely need to rethink computing. What we require are distributed federated cloud solutions with sufficient memory at the edge to cope with the large sensor data that record many medical data of individual patients. Here memory-driven computing comes in as a perfect solution. Its potential to provide sufficiently large memory at the edge, where data is generated, yet its potential to connect these new devices to build distributed federated cloud solutions will be key to drive the digital revolution in medicine. I will provide our own efforts using memory driven computing towards this direction.