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Intel Debuts 2nd-Gen Horse Ridge Cryogenic Quantum Control Chip

SANTA CLARA, Calif.–(BUSINESS WIRE)–What’s New: At an Intel Labs virtual event today, Intel unveiled Horse Ridge II, its second-generation cryogenic control chip, marking another milestone in the company’s progress toward overcoming scalability, one of quantum computing’s biggest hurdles. Building on innovations in the first-generation Horse Ridge controller introduced in 2019, Horse Ridge II supports enhanced capabilities and higher levels of integration for elegant control of the quantum system. New features include the ability to manipulate and read qubit states and control the potential of several gates required to entangle multiple qubits.

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“With Horse Ridge II, Intel continues to lead innovation in the field of quantum cryogenic controls, drawing from our deep interdisciplinary expertise bench across the Integrated Circuit design, Labs and Technology Development teams. We believe that increasing the number of qubits without addressing the resulting wiring complexities is akin to owning a sports car, but constantly being stuck in traffic. Horse Ridge II further streamlines quantum circuit controls, and we expect this progress to deliver increased fidelity and decreased power output, bringing us one step closer toward the development of a ‘traffic-free’ integrated quantum circuit.”

–Jim Clarke, Intel director of Quantum Hardware, Components Research Group, Intel

Why It Matters: Today’s early quantum systems use room-temperature electronics with many coaxial cables that are routed to the qubit chip inside a dilution refrigerator. This approach does not scale to a large number of qubits due to form factor, cost, power consumption and thermal load to the fridge. With the original Horse Ridge, Intel took the first step toward addressing this challenge by radically simplifying the need for multiple racks of equipment and thousands of wires running into and out of the refrigerator in order to operate the quantum machine. Intel replaced these bulky instruments with a highly integrated system-on-chip (SoC) that simplifies system design and uses sophisticated signal processing techniques to accelerate setup time, improve qubit performance and enable the engineering team to efficiently scale the quantum system to larger qubit counts.

About the New Features: Horse Ridge II builds on the first-generation SoC’s ability to generate radio frequency pulses to manipulate the state of the qubit, known as qubit drive. It introduces two additional control features, paving the way for further integration of external electronic controls into the SoC operating inside the cryogenic refrigerator.

New features enable:

• Qubit readout: The function grants the ability to read the current qubit state. The readout is significant, as it allows for on-chip, low-latency qubit state detection without storing large amounts of data, thus saving memory and power.
• Multigate pulsing: The ability to simultaneously control the potential of many qubit gates is fundamental for effective qubit readouts and the entanglement and operation of multiple qubits, paving the path toward a more scalable system.

The addition of a programmable microcontroller operating within the integrated circuit enables Horse Ridge II to deliver higher levels of flexibility and sophisticated controls in how the three control functions are executed. The microcontroller uses digital signal processing techniques to perform additional filtering on pulses, helping to reduce crosstalk between qubits.

Horse Ridge II is implemented using Intel® 22nm low-power FinFET technology (22FFL) and its functionality has been verified at 4 kelvins. Today, a quantum computer operates in the millikelvin range – just a fraction of a degree above absolute zero. But silicon spin qubits – the underpinning of Intel’s quantum efforts – have properties that could allow them to operate at temperatures of 1 kelvin or higher, which would significantly reduce the challenges of refrigerating the quantum system.

Intel’s cryogenic control research focuses on achieving the same operational temperature level for both the controls and silicon spin qubits. Ongoing advances in this area, as demonstrated in Horse Ridge II, represent progress over today’s brute force approaches to scaling quantum interconnects and are a critical element of the company’s longer-term quantum practicality vision.

What’s Next: Intel will present additional technical details from this research during the International Solid-State Circuits Conference (ISSCC) in February 2021.

More Context:Intel Labs Days (Press Kit) | Quantum Computing at Intel (Press Kit) | Intel Labs (Press Kit)

About Intel

Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.

© Intel Corporation. Intel, the Intel logo and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

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Supriya Venkat

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Horse Ridge II, Intel’s second-generation cryogenic control chip, brings key control functions for quantum computer operation into the cryogenic refrigerator — as close as possible to the qubits themselves — to streamline the complexity of control wiring for quantum systems. (Credit: Intel Corporation)

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Intel Labs’ Dr. James S. Clarke, director of the Quantum Hardware Research Group within Intel’s Components Research Organization, speaks as part of Intel Labs Day. Intel Labs Day 2020 was presented virtually on Dec. 3, 2020. (Credit: Intel Corporation)

Intel Machine Programming Tool Detects Bugs in Code

SANTA CLARA, Calif.–(BUSINESS WIRE)–What’s New: Today, Intel unveiled ControlFlag – a machine programming research system that can autonomously detect errors in code. Even in its infancy, this novel, self-supervised system shows promise as a powerful productivity tool to assist software developers with the labor-intensive task of debugging. In preliminary tests, ControlFlag trained and learned novel defects on over 1 billion unlabeled lines of production-quality code.

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“We think ControlFlag is a powerful new tool that could dramatically reduce the time and money required to evaluate and debug code. According to studies, software developers spend approximately 50% of the time debugging. With ControlFlag, and systems like it, I imagine a world where programmers spend notably less time debugging and more time on what I believe human programmers do best — expressing creative, new ideas to machines.”

–Justin Gottschlich, principal scientist and director/founder of Machine Programming Research at Intel Labs

Why It Matters: In a world increasingly run by software, developers continue to spend a disproportionate amount of time fixing bugs rather than coding. It’s estimated that of the $1.25 trillion that software development costs the IT industry every year, 50 percent is spent debugging code¹.

Debugging is expected to take an even bigger toll on developers and the industry at large. As we progress into an era of heterogenous architectures — one defined by a mix of purpose-built processors to manage the massive sea of data available today — the software required to manage these systems becomes increasingly complex, creating a higher likelihood for bugs. In addition, it is becoming difficult to find software programmers who have the expertise to correctly, efficiently and securely program across diverse hardware, which introduces another opportunity for new and harder-to-spot errors in code.

When fully realized, ControlFlag could help alleviate this challenge by automating the tedious parts of software development, such as testing, monitoring and debugging. This would not only enable developers to do their jobs more efficiently and free up more time for creativity, but it would also address one of the biggest price tags in software development today.

How It Works: ControlFlag’s bug detection capabilities are enabled by machine programming, a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

ControlFlag specifically operates through a capability known as anomaly detection. As humans existing in the natural world, there are certain patterns we learn to consider “normal” through observation. Similarly, ControlFlag learns from verified examples to detect normal coding patterns, identifying anomalies in code that are likely to cause a bug. Moreover, ControlFlag can detect these anomalies regardless of programming language.

A key benefit of ControlFlag’s unsupervised approach to pattern recognition is that it can intrinsically learn to adapt to a developer’s style. With limited inputs for the control tools that the program should be evaluating, ControlFlag can identify stylistic variations in programming language, similar to the way that readers recognize the differences between full words or using contractions in English.

The tool learns to identify and tag these stylistic choices and can customize error identification and solution recommendations based on its insights, which minimizes ControlFlag’s characterizations of code in error that may simply be a stylistic deviation between two developer teams.

Intel has even started evaluating using ControlFlag internally to identify bugs in its own software and firmware product development. It is a key element of Intel’s Rapid Analysis for Developers project, which aims to accelerate velocity by providing expert assistance.

More Context:Intel Labs Day (Press Kit) | MISM: An End-to-End Neural Code Similarity System | Why More Software Development Needs to Go to the Machines | Intel Labs (Press Kit) | Three Pillars of Machine Programming

About Intel

Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.

¹http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370.9611&rep=rep1&type=pdf

© Intel Corporation. Intel, the Intel logo and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

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Alexa Korkos

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KEYWORDS: United States North America California

INDUSTRY KEYWORDS: Data Management Security Technology Mobile/Wireless Software Networks Internet

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Dr. Justin Gottschlich is principal scientist and founder of Intel’s Machine Programming Research team. The team’s goal is to automate software development to reduce coding errors and address a shortage of trained expert programmers. (Credit: Intel Corporation)

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ControlFlag is a powerful tool that could dramatically reduce the time and money required to evaluate and debug code, a major industry pain point. ControlFlag was on display at Intel Labs Day 2020, which was presented virtually on Dec. 3, 2020. (Credit: Intel Corporation)

Update on Intel’s Neuromorphic Ecosystem Growth and Progress

SANTA CLARA, Calif.–(BUSINESS WIRE)–What’s New: Today, Intel shared an update on progress within the Intel Neuromorphic Research Community (INRC). The group has grown rapidly since its inception in 2018 and now includes more than 100 members, with Intel announcing today the addition of Lenovo, Logitech, Mercedes-Benz and Prophesee to explore the value of neuromorphic computing for business use cases. Additionally, Intel summarized a growing body of research results from INRC members that used the company’s neuromorphic research test chip, Loihi.

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“In two short years, we’ve formed a vibrant community comprising hundreds of researchers around the world inspired by the promise of neuromorphic computing to deliver orders of magnitude gains in computing efficiency, speed and intelligent functionality. For the first time, we are seeing a quantitative picture emerge that validates this promise. Together with our INRC partners, we plan to build on these insights to enable wide-ranging disruptive commercial applications for this nascent technology.”

–Mike Davies, director of Intel’s Neuromorphic Computing Lab

Why It Matters: Intel created the INRC because it believes no one organization alone will effectively unlock the full potential of neuromorphic computing. By collaborating with some of the leading researchers in this field spanning academia, industry and government, Intel is working to overcome the challenges in the development of neuromorphic computing and to progress it from research prototypes to industry-leading products over the coming years.

Intel and its partners have demonstrated orders of magnitude gains for real-world edge use cases and are seeing early progress in scaling these workloads to solve larger computational problems. As neuromorphic computing continues to advance, Intel and the INRC have also uncovered various potential real-world use cases for neuromorphic technology, such as enabling more efficient and adaptive robotics; rapidly searching large databases for similar content; and allowing edge devices to make difficult planning and optimization decisions in real time. The addition of Lenovo, Logitech, Mercedes-Benz and Prophesee to the INRC along with existing Fortune 500 and government members showcases the steady maturing of neuromorphic technology and its coming graduation from academic laboratories into industry applications.

Results: Through ongoing development, prototyping and testing of applications built on Intel’s neuromorphic systems, Intel and INRC members are compiling a growing body of results that show consistent gains across a wide range of workloads. Existing results, such as mimicking the human olfactory system and bringing event-based touch sensing to robotics, combined with new benchmarks outlined at Intel Labs Day, paint a picture of neuromorphic computing being well-suited for an emerging class of bio-inspired intelligent workloads that also have commercial relevance.

Benchmarking updates highlighted at Intel Labs Day include:

• Voice command recognition: Accenture tested the ability to recognize voice commands on Intel’s Loihi chip versus a standard graphics processing unit (GPU) and found Loihi not only achieved similar accuracy, it was up to 1,000 times more energy efficient and responded up to 200 milliseconds faster. Through the INRC, Mercedes-Benz is exploring how these results could apply to real-world use cases, such as adding new voice interaction commands to vehicles.
• Gesture recognition: Traditional artificial intelligence works well for crunching big data and recognizing patterns across thousands of examples, but it has a hard time learning subtle differences that change from person to person – like the gestures we use to communicate. Accenture and INRC partners are demonstrating tangible progress for utilizing Loihi’s self-learning capabilities to quickly learn and recognize individualized gestures. Processing input from a neuromorphic camera, Loihi can learn new gestures in just a few exposures. This could be applied to a variety of use cases, such as interacting with smart products in the home or touchless displays in public spaces.
• Image retrieval: Researchers from the retail industry evaluated Loihi for image-based product search applications. They found Loihi could generate image feature vectors over three times more energy-efficiently than conventional central processing unit (CPU) and GPU solutions while maintaining the same level of accuracy. This work complements similar search results from Intel’s Pohoiki Springs neuromorphic research system published earlier this year, which showed Loihi’s ability to search feature vectors in million-image databases 24 times faster and with 30 times lower energy than a CPU.
• Optimization and search: Intel and its partners have discovered that Loihi can solve optimization and search problems over 1,000 times more efficiently and 100 times faster compared to traditional CPUs. Optimization problems – such as constraint satisfaction – provide potential value at the edge, such as enabling drones to plan and make complex navigation decisions in real time. The same problem type could also be scaled for complex data center workloads, assisting with tasks like train scheduling and logistics optimization.
• Robotics: Rutgers and TU Delft researchers published new demonstrations of robotic navigation and micro-drone control applications running on Loihi. TU Delft’s drone performed optic flow landings with an evolved 35-neuron spiking network running at frequencies over 250 kilohertz. Rutgers found its Loihi solutions to require 75 times lower power than conventional mobile GPU implementations, without any loss in performance. In work published at the 2020 Conference on Robot Learning in November, Rutgers researchers found Loihi could successfully learn numerous OpenAI Gym tasks with equivalent high accuracy as a deep actor network, with 140 times lower energy consumption compared to a mobile GPU solution.

Additionally, Intel and partners unveiled two state-of-the-art neuromorphic robotics demonstrations at Intel Labs Day. Working with researchers from ETH Zurich, Intel showed Loihi adaptively controlling a horizon-tracking drone platform achieving closed-loop speeds up to 20 kilohertz with 200 microseconds of visual processing latency. This represents a 1,000 times gain in combined efficiency and speed compared to conventional solutions. Tackling the neuromorphic software integration problem, Intel and researchers from the Italian Institute of Technology (IIT) demonstrated the operation of multiple cognitive functions running together on Loihi in IIT’s iCub robot platform. These included object recognition with fast, few-shot learning, spatial awareness from those learned objects and real-time decision-making in response to human interaction.

What’s Next: As the INRC grows, Intel will continue investing in this unique ecosystem and working with members to provide technology support and explore where neuromorphic computing can add real-world value for problems big and small. Additionally, Intel is continuing to take learnings from the INRC and incorporate them into the development of the company’s next-generation neuromorphic research chip, which will be coming soon.

About the Intel Neuromorphic Research Community: The Intel Neuromorphic Research Community is an ecosystem of academic groups, government labs, research institutions and companies around the world working with Intel to further neuromorphic computing and develop innovative artificial intelligence applications. Researchers interested in participating in the INRC and developing for Loihi can visit the Intel Neuromorphic Research Community website. A list of current members can also be found at the site.

More Context:Intel Labs Day (Press Kit) | Neuromorphic Computing (Press Kit) | Intel Labs (Press Kit) | How Neuromorphic Computing Uses the Human Brain as a Model (Video)

About Intel

Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.

© Intel Corporation. Intel, the Intel logo and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

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Alexa Korkos

1-415-706-5783

[email protected]

KEYWORDS: California United States North America

INDUSTRY KEYWORDS: Semiconductor Technology Other Technology Research Science Hardware

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Intel Labs’ Mike Davies, director of the Neuromorphic Computing Lab, speaks as part of Intel Labs Day. Intel Labs Day 2020 was presented virtually on Dec. 3, 2020. (Credit: Intel Corporation)

Intel Advances Progress in Integrated Photonics for Data Centers

SANTA CLARA, Calif.–(BUSINESS WIRE)–What’s New: Today, at Intel Labs Day, Intel highlighted industry-leading technological advances toward the realization of the company’s long-standing vision of integrating photonics with low-cost, high-volume silicon. The advancements represent critical progress in the field of optical interconnects, which address growing challenges around the performance scaling of electrical input/output (I/O) as compute-hungry data workloads increasingly overwhelm network traffic in data centers. Intel demonstrated advances in key technology building blocks, including miniaturization, paving the way for tighter integration of optical and silicon technologies.

“We are approaching an I/O power wall and an I/O bandwidth gap that will dramatically hinder performance scaling. The rapid progress Intel is making in integrated photonics will enable the industry to fully re-imagine data center networks and architectures that are connected by light. We have now demonstrated all of the critical optical technology building blocks on one silicon platform, tightly integrated with CMOS silicon. Our research on tightly integrating photonics with CMOS silicon can systematically eliminate barriers across cost, power and size constraints to bring the transformative power of optical interconnects to server packages.”

–James Jaussi, senior principal engineer and director of PHY Lab, Intel Labs

Why it matters: New data-centric workloads are growing every day within the data center, with ever-increasing data movement from server to server that is taxing the capabilities of today’s network infrastructure. The industry is quickly approaching practical limits of electrical I/O performance. As bandwidth demand for compute keeps increasing, electrical I/O isn’t scaling to keep pace, resulting in an “I/O power wall” that limits available power for compute operations. By bringing optical I/O directly into our servers and onto our packages, we can break down this barrier, enabling data to move more efficiently.

About the New Technology Building Blocks: At the Intel Labs virtual event today, Intel demonstrated key progress in critical technology building blocks that are fundamental to the company’s integrated photonics research. These technology building blocks — including light generation, amplification, detection, modulation, complementary metal-oxide semiconductor (CMOS) interface circuits and package integration — are essential to achieve integrated photonics. A prototype shown at the event featured tight coupling of photonics and CMOS technologies, serving as a proof-of-concept of future full integration of optical photonics with core compute silicon. Intel also showcased micro-ring modulators that are 1000x smaller than traditional components. The large size and cost of conventional silicon modulators have been a barrier to bringing optical technology onto server packages, which require the integration of hundreds of these devices. These combined results pave the way for the extended use of silicon photonics beyond the upper layers of the network to inside the server and onto future server packages.

Key technology building blocks showcased:

• Micro-ring modulators: Conventional silicon modulators take up too much area and are costly to place on IC packages. By developing micro-ring modulators, Intel has miniaturized the modulator by a factor of more than 1,000, thereby eliminating a key barrier to integrating silicon photonics onto a compute package.
• All-silicon photodetector: For decades, the industry has believed silicon has virtually no light detection capability in the 1.3-1.6um wavelength range. Intel showcased research that proves otherwise. Lower cost is one of the main benefits of this breakthrough.
• Integrated semiconductor optical amplifier: As the focus turns to reducing total power consumption, integrated semiconductor optical amplifiers are an indispensable technology, made possible with the same material used for the integrated laser.
• Integrated multi-wavelength lasers: Using a technique called wavelength division multiplexing, separate wavelengths can be used from the same laser to convey more data in the same beam of light. This enables additional data to be transmitted over a single fiber, increasing bandwidth density.
• Integration: By tightly integrating silicon photonics and CMOS silicon through advanced packaging techniques, we can gain three benefits: lower power, higher bandwidth and reduced pin count. Intel is the only company that has demonstrated integrated multi-wavelength lasers and semiconductor optical amplifiers, all-silicon photodetectors, and micro-ring modulators on a single technology platform tightly integrated with CMOS silicon. This research breakthrough paves the path for scaling integrated photonics.

What’s Next: Integrated photonics research demonstrated at the event showcased meaningful progress toward Intel’s ambitious goal initiated many years ago to tap light as the basis of connectivity technology. The new research opens possibilities, including future architectures that are more disaggregated, with multiple functional blocks such as compute, memory, accelerators and peripherals spread throughout the entire network and interconnected via optical and software in high-speed and low-latency links.

More Context:Intel Labs Day 2020 (Press Kit) | Intel Labs (Press Kit)

About Intel

Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.

© Intel Corporation. Intel, the Intel logo and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

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Supriya Venkat

1-503-320-8024

[email protected]

KEYWORDS: California United States North America

INDUSTRY KEYWORDS: Networks Semiconductor Hardware Data Management Technology

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