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Learn more - Join the world's largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum's articles, archives, PDF downloads, and other benefits. Learn more - CREATE AN ACCOUNTSIGN IN JOIN IEEESIGN IN Close Enjoy more free content and benefits by creating an account Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE. CREATE AN ACCOUNTSIGN IN Topic Magazine Type Feature Transportation At Last, a Self-Driving Car That Can Explain Itself Mitsubishi's AI not only improves performance, it also fosters trust Chiori Hori Anthony Vetro 23 Feb 2022 8 min read Three cars, side by side on three lanes of a city street, are shown moving away from the viewer, past trees, other landmarks, and a white-striped crosswalk. Rather than tell the driver that there's an intersection 30 meters ahead, the navigation system refers to landmarks, such as the crosswalk in this illustration, or a tree farther down the street. Mitsubishi Electric Research Laboratories For all the recent improvements in artificial intelligence, the technology still cannot take the place of human beings in situations where it must frame its perceptions of the world in words that people can understand. You might have thought that the many apparent advances in speech recognition would have solved the problem already. After all, Apple's Siri, Microsoft's Cortana, Amazon's Alexa and Google Home are all very impressive, but these systems function solely on voice input: They can't understand or react to the environment around them. --------------------------------------------------------------------- To bridge this communications gap, our team at Mitsubishi Electric Research Laboratories has developed and built an AI system that does just that. We call the system scene-aware interaction, and we plan to include it in cars. As we drive down a street in downtown Los Angeles, our system's synthesized voice provides navigation instructions. But it doesn't give the sometimes hard-to-follow directions you'd get from an ordinary navigation system. Our system understands its surroundings and provides intuitive driving instructions, the way a passenger sitting in the seat beside you might do. It might say, "Follow the black car to turn right" or "Turn left at the building with a billboard." The system will also issue warnings, for example: "Watch out for the oncoming bus in the opposite lane." To support improved automotive safety and autonomous driving, vehicles are being equipped with more sensors than ever before. Cameras, millimeter-wave radar, and ultrasonic sensors are used for automatic cruise control, emergency braking, lane keeping, and parking assistance. Cameras inside the vehicle are being used to monitor the health of drivers, too. But beyond the beeps that alert the driver to the presence of a car in their blind spot or the vibrations of the steering wheel warning that the car is drifting out of its lane, none of these sensors does much to alter the driver's interaction with the vehicle. Voice alerts offer a much more flexible way for the AI to help the driver. Some recent studies have shown that spoken messages are the best way to convey what the alert is about and are the preferable option in low-urgency driving situations. And indeed, the auto industry is beginning to embrace technology that works in the manner of a virtual assistant. Indeed, some carmakers have announced plans to introduce conversational agents that both assist drivers with operating their vehicles and help them to organize their daily lives. Scene-Aware Interaction Technology www.youtube.com The idea for building an intuitive navigation system based on an array of automotive sensors came up in 2012 during discussions with our colleagues at Mitsubishi Electric's automotive business division in Sanda, Japan. We noted that when you're sitting next to the driver, you don't say, "Turn right in 20 meters." Instead, you'll say, "Turn at that Starbucks on the corner." You might also warn the driver of a lane that's clogged up ahead or of a bicycle that's about to cross the car's path. And if the driver misunderstands what you say, you'll go on to clarify what you meant. While this approach to giving directions or guidance comes naturally to people, it is well beyond the capabilities of today's car-navigation systems. Although we were keen to construct such an advanced vehicle-navigation aid, many of the component technologies, including the vision and language aspects, were not sufficiently mature. So we put the idea on hold, expecting to revisit it when the time was ripe. We had been researching many of the technologies that would be needed, including object detection and tracking, depth estimation, semantic scene labeling, vision-based localization, and speech processing. And these technologies were advancing rapidly, thanks to the deep-learning revolution. Soon, we developed a system that was capable of viewing a video and answering questions about it. To start, we wrote code that could analyze both the audio and video features of something posted on YouTube and produce automatic captioning for it. One of the key insights from this work was the appreciation that in some parts of a video, the audio may be giving more information than the visual features, and vice versa in other parts. Building on this research, members of our lab organized the first public challenge on scene-aware dialogue in 2018, with the goal of building and evaluating systems that can accurately answer questions about a video scene. We were particularly interested in being able to determine whether a vehicle up ahead was following the desired route, so that our system could say to the driver, "Follow that car." We then decided it was finally time to revisit the sensor-based navigation concept. At first we thought the component technologies were up to it, but we soon realized that the capability of AI for fine-grained reasoning about a scene was still not good enough to create a meaningful dialogue. Strong AI that can reason generally is still very far off, but a moderate level of reasoning is now possible, so long as it is confined within the context of a specific application. We wanted to make a car-navigation system that would help the driver by providing its own take on what is going on in and around the car. One challenge that quickly became apparent was how to get the vehicle to determine its position precisely. GPS sometimes wasn't good enough, particularly in urban canyons. It couldn't tell us, for example, exactly how close the car was to an intersection and was even less likely to provide accurate lane-level information. We therefore turned to the same mapping technology that supports experimental autonomous driving, where camera and lidar (laser radar) data help to locate the vehicle on a three-dimensional map. Fortunately, Mitsubishi Electric has a mobile mapping system that provides the necessary centimeter-level precision, and the lab was testing and marketing this platform in the Los Angeles area. That program allowed us to collect all the data we needed. A city street with three lanes is shown, with given vehicles moving either toward or away from the viewer; each is enclosed in a drawn-in rectangle, and on various points on the surface of each vehicle are drawn red arrows extending from green dots, producing vectors that indicate speed and direction The navigation system judges the movement of vehicles, using an array of vectors [arrows] whose orientation and length represent the direction and velocity. Then the system conveys that information to the driver in plain language. Mitsubishi Electric Research Laboratories A key goal was to provide guidance based on landmarks. We knew how to train deep-learning models to detect tens or hundreds of object classes in a scene, but getting the models to choose which of those objects to mention--"object saliency"--needed more thought. We settled on a regression neural-network model that considered object type, size, depth, and distance from the intersection, the object's distinctness relative to other candidate objects, and the particular route being considered at the moment. For instance, if the driver needs to turn left, it would likely be useful to refer to an object on the left that is easy for the driver to recognize. "Follow the red truck that's turning left," the system might say. If it doesn't find any salient objects, it can always offer up distance-based navigation instructions: "Turn left in 40 meters." We wanted to avoid such robotic talk as much as possible, though. Our solution was to develop a machine-learning network that graphs the relative depth and spatial locations of all the objects in the scene, then bases the language processing on this scene graph. This technique not only enables us to perform reasoning about the objects at a particular moment but also to capture how they're changing over time. Such dynamic analysis helps the system understand the movement of pedestrians and other vehicles. We were particularly interested in being able to determine whether a vehicle up ahead was following the desired route, so that our system could say to the driver, "Follow that car." To a person in a vehicle in motion, most parts of the scene will themselves appear to be moving, which is why we needed a way to remove the static objects in the background. This is trickier than it sounds: Simply distinguishing one vehicle from another by color is itself challenging, given the changes in illumination and the weather. That is why we expect to add other attributes besides color, such as the make or model of a vehicle or perhaps a recognizable logo, say, that of a U.S. Postal Service truck. Natural-language generation was the final piece in the puzzle. Eventually, our system could generate the appropriate instruction or warning in the form of a sentence using a rules-based strategy. A black-and-white digital image shows a multilane street lined with trees, and the trees bracketed by buildings The car's navigation system works on top of a 3D representation of the road--here, multiple lanes bracketed by trees and apartment buildings. The representation is constructed by the fusion of data from radar, lidar, and other sensors.Mitsubishi Electric Research Laboratories Rules-based sentence generation can already be seen in simplified form in computer games in which algorithms deliver situational messages based on what the game player does. For driving, a large range of scenarios can be anticipated, and rules-based sentence generation can therefore be programmed in accordance with them. Of course, it is impossible to know every situation a driver may experience. To bridge the gap, we will have to improve the system's ability to react to situations for which it has not been specifically programmed, using data collected in real time. Today this task is very challenging. As the technology matures, the balance between the two types of navigation will lean further toward data-driven observations. For instance, it would be comforting for the passenger to know that the reason why the car is suddenly changing lanes is because it wants to avoid an obstacle on the road or avoid a traffic jam up ahead by getting off at the next exit. Additionally, we expect natural-language interfaces to be useful when the vehicle detects a situation it has not seen before, a problem that may require a high level of cognition. If, for instance, the car approaches a road blocked by construction, with no clear path around it, the car could ask the passenger for advice. The passenger might then say something like, "It seems possible to make a left turn after the second traffic cone." Because the vehicle's awareness of its surroundings is transparent to passengers, they are able to interpret and understand the actions being taken by the autonomous vehicle. Such understanding has been shown to establish a greater level of trust and perceived safety. We envision this new pattern of interaction between people and their machines as enabling a more natural--and more human--way of managing automation. Indeed, it has been argued that context-dependent dialogues are a cornerstone of human-computer interaction. photo of a city street with a person crossing the street, surrounded by a drawn-in rectangle; a man on a bicycle, also in a rectangle; and in the next lane over, the back of a car Mitsubishi's scene-aware interactive system labels objects of interest and locates them on a GPS map.Mitsubishi Electric Research Laboratories Cars will soon come equipped with language-based warning systems that alert drivers to pedestrians and cyclists as well as inanimate obstacles on the road. Three to five years from now, this capability will advance to route guidance based on landmarks and, ultimately, to scene-aware virtual assistants that engage drivers and passengers in conversations about surrounding places and events. Such dialogues might reference Yelp reviews of nearby restaurants or engage in travelogue-style storytelling, say, when driving through interesting or historic regions. Truck drivers, too, can get help navigating an unfamiliar distribution center or get some hitching assistance. Applied in other domains, mobile robots could help weary travelers with their luggage and guide them to their rooms, or clean up a spill in aisle 9, and human operators could provide high-level guidance to delivery drones as they approach a drop-off location. This technology also reaches beyond the problem of mobility. Medical virtual assistants might detect the possible onset of a stroke or an elevated heart rate, communicate with a user to confirm whether there is indeed a problem, relay a message to doctors to seek guidance, and if the emergency is real, alert first responders. Home appliances might anticipate a user's intent, say, by turning down an air conditioner when the user leaves the house. Such capabilities would constitute a convenience for the typical person, but they would be a game-changer for people with disabilities. Natural-voice processing for machine-to-human communications has come a long way. Achieving the type of fluid interactions between robots and humans as portrayed on TV or in movies may still be some distance off. But now, it's at least visible on the horizon. natural-language processing transparent ai machine-human interfaces explainable ai Chiori Hori Chiori Hori is a senior principal research scientist at Mitsubishi Electric Research Laboratories in Cambridge, Mass., specializing in multimodal scene-aware interaction for human-robot communications. She earned a Ph.D. in computer science from the Tokyo Institute of Technologies. and Anthony Vetro Anthony Vetro is a vice president and director at Mitsubishi Electric Research Labs, in charge of AI research in computer vision, speech and audio processing, and in data analytics. He earned a Ph.D. in electrical engineering from Polytechnic University, in New York (now the NYU Tandon School of Engineering). The Conversation (1) [defa] Murali Karthick Baskar 25 Feb, 2022 M Amazing Article.. 0 Replies Hide replies Show More Replies A photo of a man in a blue suit and glasses in front of a logo that says "tsmc" The Institute Topic Type Careers Profile TSMC R&D Chief: There's Light at the End of the Chip Shortage 25 Feb 2022 4 min read A 3D-printed humanoid robot looks toward the camera with an outstretched arm and hand Robotics News Type Topic Video Friday: Markobot 25 Feb 2022 3 min read An illustration of an amplifier, speakers and a pair of headphones DIY Topic Hands On Type Consumer Electronics Magazine A Web-Enabled, High Quality, DIY Audio Amp 25 Feb 2022 6 min read Computing Topic Type Semiconductors News Behind Intel's HPC Chip that Will Pierce the Exascale Barrier Ponte Vecchio packs in a lot of silicon to power the Aurora supercomputer Samuel K. Moore Samuel K. Moore is the senior editor at IEEE Spectrum in charge of semiconductors coverage. An IEEE member, he has a bachelor's degree in biomedical engineering from Brown University and a master's degree in journalism from New York University. 25 Feb 2022 5 min read Textured gold rectangles at the center of a black circuit board. The Ponte Vecchio processor is destined for the Aurora supercomputer at the U.S. Argonne National Laboratory, slated to be unveiled later this year. Photo-illustration: Intel Corp. processors high-performance computing 3D integration Intel exascale supercomputers On Monday, Intel unveiled new details of the processor that will power the Aurora supercomputer, which is designed to become one of the first U.S.-based high-performance computers (HPCs) to pierce the exaflop barrier--a billion billion high-precision floating-point calculations per second. Intel Fellow Wilfred Gomes told engineers virtually attending the IEEE International Solid State Circuits Conference this week that the processor pushed Intel's 2D and 3D chiplet integration technologies to the limits. The processor, called Ponte Vecchio, is a package that combines multiple compute, cache, networking, and memory silicon tiles, or "chiplets." Each of the tiles in the package is made using different process technologies, in a stark example of a trend called heterogeneous integration. Ponte Vecchio is, among other things, a master class in 3D integration. The result is that Intel packed 3,100 square millimeters of silicon--nearly equal to four Nvidia A100 GPUs--into a 2,330 mm^2 footprint. That's more than 100 billion transistors across 47 pieces of silicon. An illustration shows groupings of labelled blue, dark grey, and tan tiles on the left and their positions on a green circuitboard on the right. Ponte Vecchio is made of multiple compute, cache, I/O, and memory tiles connected using 3D and 2D technology.Source: Intel Corp. Ponte Vecchio is, among other things, a master class in 3D integration. Each Ponte Vecchio processor is really two mirror image sets of chiplets tied together using Intel's 2D integration technology Co-EMIB. Co-EMIB forms a bridge of high-density interconnects between two 3D stacks of chiplets. The bridge itself is a small piece of silicon embedded in a package's organic substrate. The interconnect lines on silicon can be made narrower than on the organic substrate. Ponte Vecchio's ordinary connections to the package substrate were 100 micrometers apart, whereas they were nearly twice as dense in the Co-EMIB chip. Co-EMIB dies also connect high-bandwidth memory (HBM) and the Xe Link I/O chiplet to the "base silicon," the largest chiplet, upon which others are stacked. An illustration shows an exploded view of the parts of a processor each represented by different colored rectangles. The parts of Ponte Vecchio.Source: Intel Corp. Each set of eight compute tiles, four SRAM cache chiplets called RAMBO tiles, and eight blank "thermal" tiles meant to remove heat from the processor is connected vertically to a base tile. This base provides cache memory and a network that allows any compute tile to access any memory. Notably, these tiles are made using different manufacturing technologies, according to what suited their performance requirements and yield. The latter term, the fraction of usable chips per wafer, is particularly important in a chiplet integration like Ponte Vecchio, because attaching bad tiles to good ones means you've ruined a lot of expensive silicon. The compute tiles needed top performance, so they were made using TSMC's N5 (often called a 5-nanometer) process. The RAMBO tile and the base tile both used Intel 7 (often called a 7-nanometer) process. HBM, a 3D stack of DRAM, uses a completely different process than the logic technology of the other chiplets, and the Xe Link tile was made using TSMC's N7 process. An illustration shows a cut-through of a processor with large blue, black, and grey rectangles representing the silicon parts and copper showing the connections. The different parts of the processor are made using different manufacturing processes, such as Intel 7 and TSMC N5. Intel's Foveros technology creates the 3D interconnects and its Co-EMIB makes horizontal connections.Source: Intel Corp. The base die also used Intel's 3D stacking technology, called Foveros. The technology makes a dense array of die-to-die vertical connections between two chips. These connections are just 36 micrometers apart and are made by connecting the chips "face to face"; that is, the top of one chip is bonded to the top of the other. Signals and power get into this stack by means of through-silicon vias, fairly wide vertical interconnects that cut right through the bulk of the silicon. The Foveros technology used on Ponte Vecchio is an improvement over the one used to make Intel's Lakefield mobile processor, doubling the density of signal connections. Expect the "zettascale" era of supercomputers to kick off sometime around 2028. Needless to say, none of this was easy. It took innovations in yield, clock circuits, thermal regulation, and power delivery, Gomes said. In order to ramp performance up or down with need, each compute tile could run at a different voltage and clock frequency. The clock signals originate in the base die but each compute tile can runs at its own rate. Providing the voltage was even more complicated. Intel engineers chose to supply the processor with a higher than normal voltage (1.8 volts) so they could simplify the package structure due to the lower current needs. Circuits in the base tile reduce the voltage to something closer to 0.7 volts for use on the compute tiles, and each compute tile had to have its own power domain in the base tile. Key to this ability were new high-efficiency inductors called coaxial magnetic integrated inductors. Because these are built into the package substrate, the circuit actually snakes back and forth between the base tile and the package before supplying the voltage to the compute tile. A micrograph at left shows grey and white layers of the processor calling out parts that control the flow of heat. The same parts are illustrate on the right. Getting the heat out of a complex 3D stack of chips was no easy feat.Source: Intel Corp. Ponte Vecchio is meant to consume 600 watts, so making sure heat could be extracted from the 3D stack was always a high priority. Intel engineers used tiles that had no other function than to draw heat away from the active chiplets in the design. They also coated the top of the entire chiplet agglomeration in heat-conducting metal, despite the various parts having different heights. Atop that was a solder-based thermal interface material (STIM) and an integrated heat spreader. The different tiles each have different operating-temperature limits under liquid cooling and air cooling, yet this solution managed to keep them all in range, said Gomes. "Ponte Vecchio started with a vision that we wanted democratize computing and bring petaflops to the mainstream," said Gomes. Each Ponte Vecchio system is capable of more than 45 trillion 32-bit floating-point operations per second (teraflops). Four such systems fit together with two Sapphire Rapids CPUs in a complete compute system. These will be combined for a total exceeding 54,000 Ponte Vecchios and 18,000 Sapphire Rapids to form Aurora, a machine targeting 2 exaflops. It's taken 14 years to go from the first petaflop supercomputers in 2008--capable of one million billion calculations per second--to exaflops today, Gomes pointed out. A 1000-fold increase in performance "is a really difficult task, and it's taken multiple innovations across many fields," he said. But with improvements in manufacturing processes, packaging, power delivery, memory, thermal control, and processor architecture, Gomes told engineers, the next thousandfold increase could be accomplished in just six years rather than another 14. From Your Site Articles * Intel Unveils Big Processor Architecture Changes - IEEE Spectrum > * Nvidia's Supercomputing CPU Puts Intel Under Pressure - IEEE ... > Related Articles Around the Web * An Intel Delay May Have Caused a Supercomputer Deal for AMD ... > * Intel Slips, and a High-Profile Supercomputer Is Delayed - The New ... > Keep Reading | Show less Energy Topic News Type Fusion Plasmas Meet Their Match in Reinforcement Learning Has nuclear "deep fusion" cut back the years till break-even? Rebecca Sohn Rebecca Sohn is a freelance science journalist. Her work has appeared in Live Science, Slate, and Popular Science, among others. She has been an intern at STAT and at CalMatters, as well as a science fellow at Mashable. 25 Feb 2022 4 min read Image inside a tokamak fusion reactor, of a metallic torus including plasma injection and magnetic control technologies Scientists from DeepMind and the Swiss Federal Institute of Technology have developed a reinforcement-learning algorithm that controls a tokamak fusion reactor, achieving unprecedented levels of control over the typically notoriously unstable system. DeepMind tokamak deep learning artificial intelligence reinforcement learning DeepMind nuclear fusion A team of researchers at DeepMind and the Swiss Federal Institute of Technology in Lausanne, Switzerland (EPFL), has used a kind of AI called deep reinforcement learning (RL) to control the magnetic coils of a tokamak, a donut-shaped reactor used for fusion research and one of the leading candidates to generate electric power from fusion. Tokamaks create a fusion reaction within a hot plasma inside a strong magnetic field, all controlled by a structure of magnetic coils. Though AI has been used in fusion research before for things like ex post facto analysis, this is the first time it's been used to directly control a tokamak. This experimental first application of RL to tokamak control could hint at the promise of future applications of AI to help achieve higher fusion efficiencies. "We are in early days on this," said Martin Riedmiller, the head of the control team at DeepMind and an author of the new paper. He says that in the future, the conversation between AI and fusion researchers could lead to the development of completely new ways of achieving and sustaining fusion reactions. RL algorithms work by using a system of trial and error, making guesses as to what could work to come up with a solution that is the most effective. To train their algorithm, the researchers exposed the algorithm to mathematical simulations of the physics of fusion. The reinforcement learning neural net allowed scientists to "sculpt" plasma into different shapes, better enabling them to study what kinds of structure work the most effectively for fusion. "The actual quality of the underlying physics models that we use to do the simulations of fusion reactors has really, really improved," said Frederico Felici, a researcher at EPFL and another author of the paper. The researchers used a method of training called an actor-critic method, where one neural network rates the data on whether it produces a high-quality solution, while the other network takes that data and uses it to control the fusion reaction. After the algorithm was trained using the simulated environment, the researchers tested it out with an actual tokamak--the Variable Configuration Tokamak (also called Tokamak a Configuration Variable or TCV) at EPFL. First, the researchers used traditional control methods to form the plasma and establish its location and current. Then they "handed off" control to the RL system. Because making changes to the actual fusion process can be dangerous and destructive, the algorithm was not trained at all by the actual reaction and only received training from simulations--a "zero-shot" transfer from training to the real world, as the researchers write in their paper. "It's extremely important that they were able to show that we were able to build this model using the simulated environment and apply it--and it actually worked on the real experiment," said Chris Hansen, a senior research scientist at the University of Washington who does research on fusion and plasma science and was not involved in the study. "You want [the algorithm] to work from day one." The researchers initially tested the system by starting the experiment, increasing the instability of the plasma, and bringing it back down the plasma's initial condition. After this basic test, the group experimented with different plasma configurations. In essence, they were able to "sculpt" plasma into different shapes, better enabling them to study what kinds of structure work the most effectively for fusion. The team created more typical elongated, oval-like shapes, as well as one nicknamed the "snowflake" configuration, as well as one that looks like a triangle on its side. They also formed a pattern of two independent "droplets" of plasma in the reactor for the first time. 3D rendering of the Swiss Federal Institute of Technology's experimental tokamak fusion reactor This cutaway rendering of the Swiss Federal Institute of Technology's experimental tokamak fusion reactor reveals the complex layers of plasma equilibrium coils involved. DeepMind "The shape has a fundamental effect on the quality of the plasma confinement, so how well the plasma keeps the heat inside, and on the stability of the plasma--to what extent it is prone to whatever unstable events might happen," said Felici. Events like disruptions, in which the plasma escapes the magnetic field, interrupt the reaction and can even cause damage. This RL-based control method is simpler than other methods for fusion control, though it's not necessarily more effective. For instance, the usual method of controlling a tokamak uses several independent controllers working in tandem. The new method replaces this system with a single controller. In the future, the researchers hope to come up with ways to simulate and study the internal dynamics of different plasma configurations, not only magnetic control of the reactor's coils. Using RL also has some inherent disadvantages, they add. Any deep learning system, after all, is a "black box." Because the ways the system comes to its conclusions aren't obvious, there wouldn't be a clear way to know what happened if something went wrong. Nevertheless, RL as a method of plasma control exhibits clear advantages in actually controlling the otherwise notoriously unstable system, which is why the researchers continue to express such optimism--and why they'll be continuing to improve upon their initial successes. "It's just really exciting to see where this can go in the future," said Hansen. The results of the group's work were published earlier this month in the journal Nature. From Your Site Articles * Has Fusion Really Had Its "Wright Brothers" Moment? - IEEE Spectrum > * 5 Big Ideas for Making Fusion Power a Reality - IEEE Spectrum > * Can AI Make a Better Fusion Reactor? - IEEE Spectrum > Related Articles Around the Web * The Future of Atoms: Artificial Intelligence for Nuclear Applications ... > * DeepMind's AI can control superheated plasma inside a fusion ... > Keep Reading | Show less Consumer Electronics Webinar PCI Express Gen 3: Compliance and Debug Testing Now available on-demand Rohde & Schwarz 23 Feb 2022 1 min read Rohde & Schwarz Logo Rohde & Schwarz type:webinar This webinar is intended for engineers who work on high-speed digital design and test. In particular, we will be focusing on PCIe Gen 3 interfaces. After an overview of PCIe technology, we will be discussing PCIe testing for compliance, protocol trigger and decode, and signal integrity debug purposes. 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