At Intel Labs Day 2020, Intel spotlighted research initiatives across multiple domains where its researchers are striving for orders of magnitude advancements to shape the next decade of computing. Themed “In Pursuit of 1000X: Disruptive Research for the Next Decade in Computing,” the event featured several emerging areas including integrated photonics, neuromorphic computing, quantum computing, confidential computing and machine programming. Together, these domains represent pioneering efforts to address critical challenges in the future of computing, and Intel’s leadership role in pursuing breakthroughs to address them. Rich Uhlig, Intel senior fellow, vice president and director of Intel Labs, was joined by several domain experts across the research organization to share perspectives on industry and societal impact of these technologies.
Intel Labs’ Rich Uhlig keynote: “In Pursuit of 1000X: Disruptive Research for the Next Decade of Computing.” The keynote includes various Intel Labs leaders on the areas of integrated photonics, neuromorphic computing, quantum computing, confidential computing and machine programming. (Credit: Intel Corporation)
» Keynote Presentation Slides
A brief overview of the featured domains and related disclosures from Intel Labs Day:
Integrated Photonics: Technology updates on the next evolution of Intel’s silicon photonics journey, which is looking to overcome the limits of electrical I/O by advancing the integration of optical and silicon technologies for future data centers and networks connected by light.
Disclosure: Key technology advances in integrating photonics with CMOS silicon, paving the way for tighter integration of optical and silicon technologies.
Neuromorphic Computing: Insights on brain-inspired architecture for energy-efficient AI at the edge.
Disclosure: An update on the Intel Neuromorphic Research Community’s growth and benchmark results, including the addition of new corporate members and numerous new benchmarking updates computed on Intel’s neuromorphic test chip, Loihi.
Quantum Computing: Intel’s perspective on what it will take to achieve “Quantum Practicality.”
Disclosure: The introduction of Horse Ridge II, Intel’s second-generation cryogenic quantum control technology, which builds on progress in scaling quantum interconnects with enhanced controller capabilities, programmability and micro-controller integration.
Confidential Computing: A spotlight on federated learning and homomorphic encryption, two security-focused research initiatives that expand on today’s confidential computing to eliminate additional barriers that prevent free and full sharing and use of data.
Disclosure: Today, Intel — in collaboration with partners — launched the Private AI Collaborative Research Institute to advance and develop technologies in privacy and trust for decentralized AI. The Institute has selected the first nine research projects across eight universities worldwide.
Machine Programming: An expert view on this emerging area of research with the potential to disrupt software programming as we know it today.
Disclosure: A novel machine programming research tool called ControlFlag that could increase developer productivity by automating debugging and other time-consuming tasks to improve the quality of software.
Intel Labs is a global research organization committed to discovering and developing new technologies and compute forms to unleash the exponential power of data. As data becomes more deeply embedded in our lives with every technology leap — from artificial intelligence (AI) to 5G to the intelligent edge -– Intel Labs’ role is to find new ways to unleash its full potential. The global team of researchers bring their fearless creativity and interdisciplinary expertise to the data challenges of the future, inventing breakthroughs that scale for broad societal impact. Press Kit: Intel Labs
Intel Labs Day 2020: Rich Uhlig closing session. (Credit: Intel Corporation)
Quantum computing is a disruptive, new computing paradigm based on the principles of quantum physics. Quantum computing, in essence, is the ultimate in parallel computing, with the potential to tackle problems that classical computers can’t handle. Intel is taking a full-stack approach to quantum research — spanning hardware, software, compilers, algorithms and applications — which is essential for building useful, practical quantum systems that can deliver real-world benefits across every field: from cryptography and security to drug discovery to things we can’t yet imagine. We call this quantum practicality. Intel’s strengths in materials science and semiconductor manufacturing at scale will be important to realizing the promise of quantum practicality. Press Kit: Quantum Computing
Intel is focusing its quantum hardware research in three areas that are strongly differentiated from other approaches in the industry:
- Intel is leveraging its proven deep expertise in the design and manufacturing of transistors at massive scale by using silicon spin qubits, which closely resemble transistors.
- Intel is building a custom, highly-integrated system-on-chip (SoC) called Horse Ridge for quantum controls that is designed to operate at cryogenic temperatures and minimize the complexity of quantum interconnects as those systems scale.
- Intel is investing in capabilities like a custom-designed cryoprober that dramatically speeds up time-to-information for our quantum testing and validation workflows.
Intel’s James Clarke presents “From a Grain of Sand to a Quantum Bit: Advances in Qubit Design and Control.” (Credit: Intel Corporation)
» James Clarke’s Quantum Computing Presentation Slides
Intel’s Anne Matsuura presents “A Full-stack Scalable Approach to Quantum Systems,” with guest speaker, Professor Fred Chong, University of Chicago. (Credit: Intel Corporation)
» Anne Matsuura’s Quantum Computing Presentation Slides
Intel Cryoprober: Accelerated Wafer Spin Qubit Testing — Intel leverages its semiconductor manufacturing knowledge and infrastructure to fabricate thousands of spin qubit devices daily. (Credit: Intel Corporation)
Neuromorphic computing is a complete rethinking of computer architecture from the bottom up. The goal is to apply the latest insights from neuroscience to create chips that function less like traditional computers and more like the human brain. Neuromorphic systems replicate the way neurons are organized, communicate and learn at the hardware level. Intel sees its Loihi research chip and future neuromorphic processors defining a new model of programmable computing to serve the world’s rising demand for pervasive, intelligent devices. Press Kit: Neuromorphic Computing
Intel’s Mike Davies presents “Early Benchmarking Results for Neuromorphic Computing: What It Reveals About Future Adoption.” (Credit: Intel Corporation)
» Neuromorphic Computing Presentation Slides
Intel’s Mike Davies opens for Accenture, which presents “How Neuromorphic Computing Will Help Industries Drive AI at the Edge.” (Credit: Accenture)
Accenture’s ‘Automotive Voice Command’ — Accenture Labs built “Automotive Voice Command,” a proof-of-concept system running on Intel’s neuromorphic test chip, Loihi, which demonstrates that neuromorphic computing can make cars smarter without draining the batteries. (Credit: Accenture)
Accenture’s Adaptive Control Algorithm — Built for individuals with spinal injuries, Accenture Labs built a proof-of-concept adaptive control algorithm to control the Jaco assistive robotic arm and implemented the algorithm on Intel’s Kapoho Bay chip. This adaptive control is displayed on an assistive robot arm demo. (Credit: Accenture)
Accenture’s Gesture Recognition Algorithm — Accenture is working on a gesture recognition algorithm, run on Intel’s Loihi neuromorphic research chip, with the ultimate goal of allowing users to interact with their vehicles not just through voice but through gesture. (Credit: Accenture)
One-shot Object Learning for Robots with the Loihi Test Chip — Intel’s I-Cub Robot demonstrates how neuromorphic hardware will support the next generation of artificial intelligence systems for robots that are efficient and configurable, and can learn after deployment. Intel Labs Day 2020 was held virtually on Dec. 3, 2020. (Credit: Intel Corporation)
Ultra-Fast Vision-based Drone Control with the Loihi Test Chip — An Intel Labs demo shows a system that seamlessly integrates vision and control using neuromorphic technology: the event-based Dynamic Vision Sensor and Intel’s neuromorphic research chip Loihi. (Credit: Intel Corporation)
The field of machine programming — the automation of the development of software — is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In today’s technological landscape, software is integrated into almost everything we do, but maintaining software is a time-consuming and error-prone process. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code. Intel realizes the pioneering promise of machine programming, which is why it created the Machine Programming Research (MPR) team in Intel Labs. The MPR team’s goal is to create a society where everyone can create software, but machines will handle the “programming” part.
Intel’s Justin Gottschlich presents “The Promise of Machine Programming Today, Tomorrow and Into the Future.” (Credit: Intel Corporation)
Intel’s Justin Gottschlich presents “Developer Productivity in the XPU Era.” (Credit: Intel Corporation)
» Machine Programming Presentation Slides
ControlFlag: Autonomous Software Anomaly Detection — ControlFlag’s bug detection capabilities are enabled by machine programming, a fusion of machine learning, formal methods, programming languages, compilers and computer systems. (Credit: Intel Corporation)
Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names — multiparty computing, federated learning and privacy-preserving analytics, among them. Confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance.
Intel Labs Principal Engineers Jason Martin and Rosario Cammarota present “Confidential Computing: Advances in Federated Learning and Fully Homomorphic Encryption.” (Credit: Intel Corporation)
» Confidential Computing Presentation Slides
Federated Learning Using Intel SGX — A Federated Learning model built using Intel software guard extensions provides trust by maintaining privacy of contributed code and data for collaborations. (Credit: Intel Corporation)
Intel spearheaded the field of silicon photonics with scientific breakthroughs and prototypes demonstrations and effectively transferred the technology to its data center businesses. Today, there are more than 4 million Intel 100G transceivers with integrated lasers being used by its customers for rack-to-rack connectivity — row switches and director-class spine switches that connect the network. This is where silicon photonics is shining right now, with millions of units operating between the switches in the network. Integrated photonics represents the next chapter in the company’s long-standing vision of integrating photonics with low-cost, high-volume silicon, which could bring the power of optical interconnects to server packages. Such a transformation could pave the way to reimagine data center networks and architectures of the future that are connected by light.
Intel Labs Senior Principal Engineer James Jaussi presents a session on “Transitioning Server Interconnect from Electrical to Optical I/O.” (Credit: Intel Corporation)
Transitioning Server Interconnect from Electrical to Optical I/O — With Integrated Photonics, Intel is working to bring optical I/O directly to servers. (Credit: Intel Corporation)
Building Future Technologies on Light (Technical Presentation) — Principal Engineers Haisheng Rong and Ganesh Balamurugan, from Intel’s PHY Research Lab, speak on Intel’s vision for integrated photonics and highlight current innovations that have brought the company to where it is today. (Credit: Intel Corporation)
“New embedded architectures, such as Loihi, are key to maximizing the potential of machine learning in battery-operated appliances,” said Jean-Michel Chardon, Logitech’s head of AI
integration of computing and storage envisioned by the neuromorphic computing would enable data parallelism and distributed processing. This would lead to massive amounts of data being processed much faster in real time and on a much smaller footprint,” said Dr. Yong Rui, Lenovo Corporate CTO and SVP. ” It could be one of the most important possibilities and trends in the future development of the AI technology. Lenovo hopes that the new-generation AI architecture could bring about new applications, and transform how people would interact with intelligent devices and services, and how industries would operate.”
“We look forward to exploring within INRC whether neuromorphic hardware can help us achieve higher energy efficiency, greater speed and even greater accuracy for our vehicle-related AI applications,” said Jasmin Eichler, Director Future Technologies, Mercedes-Benz AG. “The INRC offers us another exciting platform to network with research groups around the world. Within this framework, we would like to make a contribution to this promising field of research with our DAIMLER group research. With the knowledge we’ll gain, we want to achieve a significant boost for our AI applications in and around our vehicles. For us, this is an interesting part in our strive for being a leader in the field of automotive technology.”
“Artificial intelligence, in particular in the form of integrated at-the-edge sensing-processing systems, is going to penetrate many aspects of our everyday life. Neuromorphic biology-inspired approaches to both sensing and processing promise to be at the forefront of this evolution. Prophesee looks forward to leveraging the inherent natural fit between our neuromorphic sensors and AI algorithms and Intel’s Loihi Spiking Neural Networks technology for developing even higher performing solutions for artificial vision, with an aim toward enhancing safety, efficiency and performance in key vision-enabled applications such as automotive, Industry 4.0 and IoT”, said Christoph Posch, CTO and co-founder of Prophesee.