Almost five billion Internet users are using, creating and sharing ginormous amounts of data. 

Even as the amount and types of data increase, the number of opportunities to create and share data has exploded across devices ranging from home security systems, appliances, gaming systems, computers and phones to huge data centers that handle social media, streaming content, games and enterprise applications. A study by Ericcson notes there will be more than 42 billion connected IoT devices generating ~177 ZB of data (by 2026). 

One report notes that 2.5 quintillion bytes of data are created every day and 90 percent of the world’s data has been created in the last two years. Much of that data has been driven by increases in almost four billion users of social media sites worldwide.

Not surprising, given remote learning and work-from-home, the ongoing pandemic has only further increased the world’s hunger for more data -and more bandwidth to share the data. According to one industry report, home data usage increased 38 percent from March 2019 to March 2020. The same report found that work-from-home increased during the pandemic from an average of 17 percent of workers to 44 percent, putting increased strain on networks and increasing data usage.  

Covid-19 also resulted in an increase of 138 percent in a group called power users consuming more than 1 terabyte of Internet data quarterly. Google alone counts more than 63,000 searches every second, or 5.6 billion daily searches.  
 

Amir Faintuch, SVP & GM, Computing & Wired Infrastructure SBU at GF explains why silicon photonics is a vital technology platform for the data revolution.

These factors do not begin to comprehend potential impacts of the emerging metaverse, which will demand the creation, storage and connectivity of even more data – data that will need to be transmitted with very low latency at high speeds. The metaverse joins AI, machine learning and virtual reality as well as the continued expansion of connected devices in driving data creation and transmission. 

In the data center, power consumption has become a key consideration along with bandwidth.  

Historically, the chip industry has relied on electrical connectivity over metal (copper) connections for interconnects between systems. Electrical SerDes (serial deserializers), the most common form of electrical I/O, is reaching its limits and there is no attainable roadmap beyond 112 Gb/sec because the large signal losses in copper-based interconnects at a board level make it difficult to transmit data further than a few centimeters at such a high data rate.  

The next wave of high-performance computing architectures requires a new form of I/O that avoid the bottlenecks created by electrical I/O. By 2028, most data center short-distance physical interconnects will be optical instead of electrical.  

The pluggable module has been the key widget that converts electrical signals to optical signals and vice versa i.e. it is the electro-optic interface. 

There have been two key advantages of pluggable modules: 

• Standardization and interoperability – data center operators can source modules from multiple vendors which has driven down the “per Gbps” cost through innovation and competition. 
• Modularity – data center operators can use short range optics to traverse to the end of a row of racks in a data center, and long-range optics to go longer distances to a different data center. 

This modularity has also driven the form factor and standardization of the switch boxes and the box faceplates into which these pluggable modules are plugged into. In a typical switch box at the top of a data center rack, multiple pluggable modules are plugged into the faceplate. The conversion from optical to electrical signals occurs at the faceplate with high-speed electrical signals having to traverse the Cu traces on the board to the switch ASIC. 

Optical communications solutions are poised to enable new levels of performance in hyperscale data centers, cloud computing and 5G-driven network transformation. The silicon photonics technology, used for optical communications, will also become the foundation for rapidly emerging compute and sensing applications. 

Look for our next installment in this series to understand the role of new options to combine the proven benefits of CMOS technology with new capabilities for more powerful chips based on silicon photonics.   

by Gary Dagastine

Artificial intelligence (AI) technology has made great strides in recent years, evolving from limited use in a small number of applications into an essential enabler of the systems that now pervade our lives.

“Smart” thermostats, doorbells and voice assistants; semi-autonomous vehicles; medical monitoring devices with predictive capabilities; and a myriad of other applications in many fields now rely on AI technology.

But AI and its specialized subsets (machine learning, deep learning and neuromorphic computing) have an Achilles Heel that stands in the way of further progress: a huge and growing energy appetite. As AI computing becomes more demanding and its overall use grows, the amount of energy required for AI computations and data transport is rapidly increasing, leading to excessive use of energy resources and to a significantly increased global carbon footprint.

This growth in energy usage is unsustainable. Consider data centers, which make heavy use of AI. In 2017 they consumed about three percent of all the electrical power in the U.S, but by 2020 that had doubled to six percent, and there’s no end in sight. Industry projections say that by 2041 data centers theoretically would consume the world’s entire energy output if today’s inefficient compute architectures were still in use.

AI’s energy challenge isn’t restricted to data centers. Battery-powered Internet of Things (IoT) devices at the network edge also have large power requirements, in the aggregate. As more AI processing moves to the edge, increasingly sophisticated IoT devices must become much more efficient so that their lithium-ion batteries can power more functions, last longer and/or be made physically smaller. That also would help to reduce the growing volumes of potentially hazardous Li-ion waste from discarded batteries.

GlobalFoundries (GF) has aligned its product roadmap to address the AI energy challenge, by incorporating a series of technical innovations into its 12LP/12LP+ FinFET solution (used in data centers and IoT edge servers) and 22FDX® FD-SOI solution (used at the IoT edge). In addition, GF is working with leading AI researchers to develop new, more efficient computing architectures and algorithms to open up new AI horizons.

A Paradigm Change for AI

An AI system gathers large amounts of either structured or unstructured data and then processes it according to an algorithm written for a given application. The goal is to find relevant correlations and patterns within the data, to make inferences and decisions based on them, and to act on those inferences in a way that satisfies the needs of the application. Intensive computer processing is required, given the size of the data sets and the sophistication of the algorithms.

“At the present time most AI tasks are running in the cloud, but the data sets that are fed into the algorithms in the cloud come in from the outside world, through an analog interface like an IoT device on the edge,” said Ted Letavic, CTO and VP Computing and Wireless Infrastructure (CWI) at GF. “The cloud-based AI paradigm is energy inefficient, as it requires the transport of large amounts of data from the edge of the network (IoT edge) to the datacenter where the computations are performed and results derived, and subsequent transport of the results back to the edge device. Not only is this energy inefficient, the time associated with data transport results in an overall system latency which precludes use for many safety-critical AI applications.”

At first, traditional general-purpose central processing units (CPUs) were used for AI and machine learning. “These were designed for random memory access, which has become problematic given the growing need to reduce the time and energy spent transferring data between processors and memory,” Letavic said. “We need to change the paradigm, and process the data stored within the memory network itself without having to transport it.”

As a result, he said, a fundamental shift in computing architectures is taking place. A “renaissance of design” is occurring towards domain-specific compute architectures which are extremely energy efficient for AI inference (training) tasks which include well-defined dataflow and compute paths. These optimized accelerators resemble memory hierarchies, often referred to as “digital compute-in-memory” or “analog compute-in-memory.” These accelerators perform parallel operations making them ideal for the type of computations at the heart of AI, and at substantially lower total power which enables greater use of AI at the network edge.

4X More Efficient Memory with GF’s 12LP+

To accommodate these changes in architecture, GF has made technology improvements and enabled new design flows.

“In virtually every single AI workload we examined, memory bandwidth and memory access power limited overall capabilities, because a certain number of operations must take place within a fixed power budget, and memory consumed far too much of it,” Letavic said. “So we applied some learnings from our 7nm technology development effort to our 12LP/LP+ technology, and came out with the industry’s first 1 GHz-capable 0.55V SRAM memory macros, which for typical workloads reduce the energy associated with memory access by a factor of four. This solution is targeted at systolic array processors and is directly applicable to AI and machine learning workloads.”

Next, GF looked at the array architectures, Letavic said.

“We found that every single customer had a different dataflow architecture and there was basically no way to select an optimum design,” he said “To address this, we created a novel design flow that synthesizes logic and memory elements together so they can be built in very close proximity with a high degree of flexibility. This design flow breaks the conventional paradigm of logic and memory macro synthesis, and the intermingling of logic and memory elements can be used to implement very novel AI architectures.”

Advances in GF technology, coupled with a new and unique design and synthesis flow, are powerful tools for the implementation of new compute paradigms, Letavic said, and further unlock the promise of AI. Important work in this area is taking place in collaboration with leading research institutions.

Dr. Marian Verhelst and GF’s University Connection

GF is collaborating with some of the world’s leading researchers to study these novel architectures and establish objective benefits and proof points for them, which GF’s customers then could use to design more efficient AI systems.

Much of this work is taking place through collaborations with research consortia such as imec, and with university professors through GF’s University Partnership Program (UPP). Under the program, GF works closely with worldwide academic researchers on innovative projects leveraging GF technology.

One of GF’s leading academic collaborators is Dr. Marian Verhelst, Professor at the KU Leuven university in Leuven, Belgium, and also a research director at Imec. Dr. Verhelst is one of the world’s leading experts in highly efficient processing architectures. She previously worked at Intel Labs in the U.S. on digitally enhanced analog and RF circuits, and came to KU Leuven in 2012 where she started a research lab, which currently has 16 doctoral students and postdoctoral researchers.

Her lab’s work encompasses everything from long-horizon, big-picture projects funded by the European Union, to nearer-term efforts that involve technology transfer to a wide range of industry players. She has been awarded Belgium’s André Mischke YAE Prize, which recognizes internationally leading academic research, management, and evidence-based policy making.

A former member of the Young Academy of Belgium and the Flemish STEM platform, she is an outspoken advocate for science and education, and has been featured on several popular science shows on national television. In 2014, she founded InnovationLab, which develops interactive engineering projects for high school teachers and their students. She also is a member of the IEEE’s Women in Circuits initiative, among many other advocacy and educational activities.

The DIANA Chip – a Significant Step Forward for AI

Dr. Verhelst has led an effort to produce a hybrid neural network chip which is the world’s first chip to not only combine analog compute-in-memory and digital systolic arrays, but which can seamlessly partition the AI algorithm across these heterogenous resources to achieve optimum energy performance, accuracy, and latency.

Called DIANA (DIgital and ANAlog), the chip was built using GF’s 22FDX platform and will be featured in a paper to be delivered later this month at the prestigious 2022 International Solid State Circuits Conference (ISSCC).

“Machine learning is booming and everyone has a processor optimized for machine learning, but mostly they’ve been designed purely in the digital domain, and they compute using zeros and ones, which isn’t always the most efficient thing you can do,” Verhelst said. “Therefore, many researchers are now investigating computing in the analog domain, even inside SRAM memories, working with current accumulation across SRAM cells instead of with zeroes and ones. That can be much more efficient from an energy point of view, and also from a chip-density point of view because it allows you to do more computing per square millimeter.”

“There have been some excellent results thus far, but only for specific machine learning networks which happen to nicely match the shape of the memories. For others, the algorithms don’t necessarily run efficiently,” she said. “The DIANA chip contains a host processor along with both a digital and an analog-in-memory co-processor. For every layer of a neural network, it can dispatch a given layer to the inference accelerator or co-processor that will operate most efficiently. Everything runs in parallel and intermediate data is efficiently shared among the layers.”

To achieve this, Verhelst’s team developed advanced schedulers and mappers, which analyze a chip’s hardware characteristics to determine either the most energy-optimal or most latency-optimal “order of compute,” or how to run a given algorithm on the chip.

“There are many ways to run an algorithm, depending on how much memory you have, its characteristics, how many compute elements there are in your processing array, and so on,” she said. “So we developed tools into which you can enter the hardware characteristics, and which help to find the optimal solution for your workload.”

An Ongoing Collaboration

The DIANA chip is the latest result of Verhelst’s work with GF, which began about five years ago when GF offered the opportunity for one of her Ph.D. students to tape out a video processing chip on 22FDX technology, which could efficiently carry out hundreds of operations in parallel.

Subsequently, Verhelst worked on GF’s 12 LP+ technology to build a deep-learning chip for a very dense compute fabric, with more than 2,000 multipliers on the chip and large SRAM content. Yet another project that is in initial stages is to use GF’s 22FDX platform to build a heavily duty-cycled machine learning chip with a focus on extremely low-power operation for IoT, machine monitoring or other sensor nodes that must operate on milliwatts of power.

She says that the silicon access and technical partnership which GF provides is invaluable. “Producing working silicon can be very expensive, especially for digital processors which are physically large. Working with GF provides us both a lower barrier to silicon and access to the latest relevant IP,’ she said.

“Also, GF provides us with advice and support for what are sometimes difficult physical design closure jobs, which isn’t necessarily trivial any longer given these advanced technologies. There are so many things you have to take into account in the backend, that GF’s manufacturing experience really helps us when we are trying to ensure things like fast IO, good oscillators, optimum power gating and so on.”

Looking Ahead

When asked what’s next for GF with regard to more energy-efficient AI, Letavic mentioned the company’s work with integrated voltage regulation for the compute die itself, and silicon photonics for even higher levels of transport and compute efficiencies.

“Improved power delivery is a way to compensate for the lack of power scaling at smaller nodes, which has become a real limitation at the systems level,” he said. “One of the key ways to save total application power is just to be more efficient at the way you deliver current and voltage to the processor core. We’re exploring various options, and it could be a very large opportunity for GF given our long heritage in bipolar CMOS and DMOS power devices.”

Letavic also mentioned that photonic acceleration, or using light (photons) instead of electricity (electrons) not only to transmit signals over optical fiber but for computing itself, may come to play a significant role in AI. “I would say this is developing a rate much faster than I had expected. And it’s another place where we have some really solid university engagements.”

Read about other research taking place through’s GF’s University Partnership Program:

• GF Drives Progress in Next-Generation Automotive Radar
• Academic Collaborations Strengthen, Hasten GF’s Path to 6G Leadership
• GF Partners with Leading Researchers on 6G Technologies

By Emma Cheer

February is Black History Month in the U.S. The month-long observance is an opportunity to recognize and pay homage to the contributions, innovations, and trailblazers within the Black community who have elevated business, technology, art, music, science and other fields that impact the lives of people around the world.

GlobalFoundries (GF) knows the best ideas come from a diverse team being inclusive, and that our success rests on empowering employees to bring their whole person — 15,000 employees with unique talents and distinctive qualities — to our company. Building a culture of inclusion drives better business outcomes. A critical driver of diversity at GF is our employee-led Employee Resource Groups (ERGs), which foster a diverse, inclusive workplace aligned with GF’s organizational mission, values, and goals.

Established in August 2020, the Black Resource Affinity Group (BRAG) at GF seeks to embrace the diverse experiences of Black employees and provides a safe place to express individualism, while continuing to build upon GF’s inclusive culture. BRAG focuses on promoting the recruitment, retention, and professional advancement of Black employees. BRAG has grown to over 50 members and allies, with hundreds of GF employees in attendance at recent events.

“As an ESG leader for BRAG at GF, I want to foster a network for our Black community and bring attention to the challenges and barriers faced by our Black, Indigenous and People of Color,” said Arleea Hendricks, lead HR business partner at GF and a member of the Fab 8 team in Malta, New York. “Black History Month is a time to commemorate and reflect on the remarkable achievements of African Americans throughout history, as well as an opportunity to increase visibility and have conversations around social injustice and cultural acceptance.”

Through a truly remarkable year for GF, BRAG remained steadfast in supporting its members with networking events, professional development opportunities and outreach efforts beyond GF. Here’s a snapshot of BRAG’s year in review. Developing Black Talent within GF

Providing professional development opportunities is the key to BRAG’s objectives. In September, BRAG hosted a panel on growth, featuring GF leaders across business units and inviting all GF team members to participate in an interactive panel session to gain confidence in their career journey. A main topic of the discussion focused on navigating role changes and exploring career opportunities within GF.

Another top priority for BRAG is creating networking opportunities for its members, through mentorship activities, meet and greets for GF interns, and presentations from diversity and inclusion leaders such as Dereca Blackmon and Dr. Wanda Heading Grant.

GF’s Vice President and Chief Accounting Officer Will Billings, who joined the company last year, took on the role of BRAG’s new executive sponsor. With more than two decades of experience in accounting and finance, he brings to GF and BRAG a wealth of experience of working on and leading diverse international teams.

Building Relationships through Diversity and Inclusion Initiatives

Last February, BRAG partnered with GF’s corporate and employee giving program, GlobalGives, to showcase Black-run nonprofits, including All Star Code, The Hidden Genius Project, Sad Girls Club and Fighting 4 the Tatas. These nonprofits provide medical and professional support for Black men and women. In addition to the 2021 GlobalGives campaign, BRAG members worked tirelessly to support local school and community events throughout the year.

At GF, one of our core values is “Embrace.” Events like BRAG’s Lunch & Learn series allow for engaging discussions on allyship and inclusion in a trusting environment. These are important for reaffirming this value as we take the time to reflect on what allyship and inclusion mean at GF and how we can continuously improve our company culture.

In April, BRAG collaborated with Malta GlobalWomen to host “Preventing Microaggressions in the Workplace.” Collaboration between ERGs opens opportunities for team members to create connections both within our sites and across the globe.

Partnering for a More Inclusive GF

GF believes that a diversity of ideas and backgrounds makes for a stronger culture of trust and innovation. Partnering with organizations such as The McKinsey Leadership Program, AfroTech, the Jackie Robinson Foundation and more enables GF to grow as a diverse yet united ONEGF.

A group of 22 outstanding GF leaders participated in the McKinsey Black Leadership Academy’s pilot Management Accelerator program. The Management Accelerator program is a 6-month practical “mini-MBA” tailored to the unique experience of Black leaders, building foundational skills for early to mid-career leaders, developing core leadership and management capabilities through a case-based approach. McKinsey Leadership specifically acknowledges the unique skills of Black leaders and the challenges they face.

In November, GF was a proud sponsor of AfroTech, one of the largest conferences for Black tech innovators.

BRAG member and Senior Engineer Marvin Montaque attended the virtual event. “The AfroTech Conference of 2021 was an incredible experience. The ability to meet with industry leaders from well-known companies to companies not even on my radar was amazing,” he said.

“We sometimes forget that there is an engine – people- behind every company with knowledge and experiences that can impact your own world,”’ Montaque continued. “AfroTech 2021 provided a unique opportunity to interact with these folks via the metaverse. This technology blows my mind thinking about it. I was able to take friends on a boat ride (virtually) while catching up and meeting new people. I was also able to hop from event to event across campus in a matter of seconds. The content was also amazing, from attending workshops and pitch competitions, meeting with executives from Disney/Slack and picking their brains on what is to come in their industry, to attending DJ/dance parties from my living room. The whole experience was incredible.”

GF continues to build its partnership with the Jackie Robinson Foundation (JRF). This partnership focuses on advancing higher education opportunities for underrepresented minorities by providing multi-year scholarship awards to highly motivated college students with an interest in STEM. GF hosted two JRF scholars as interns in the summer of 2021.

“This past summer was one of my greatest learning experiences,” said Mbaba Sow, a JRF scholar at GF. “Being at GlobalFoundries in Malta, New York allowed me to take a deep dive into the semiconductor manufacturing industry. Being exposed to the integrated systems that control the semiconductor chip process, and being able to get up from my desk and go see the process live was amazing. My co-workers in my department were very welcoming and were willing to answer any questions that I had for them. By the end of my internship, I felt like I added members to my family.”

GF is proud of BRAG’s accomplishments and looks forward to the next year of excellent programming and learning opportunities. Interested in learning about other Employee Resource Groups at GF? Read our ERG Round-up: Employee Resource Groups Driving Diversity, Inclusion, and Success at GlobalFoundries