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Computer Science

Powering AI and ML: Unveiling GDDR6’s Role in High-Speed Memory Technology

Powering AI and ML: Unveiling GDDR6's Role in High-Speed Memory Technology

Introduction

Artificial intelligence (AI) and machine learning (ML) have evolved into game-changing technologies with limitless applications ranging from natural language processing to the automobile sector. These applications need a significant amount of computing power, and memory is an often neglected resource. Fast memory is crucial for AI and ML activities, and GDDR6 memory has established itself as a prominent participant in this industry where high speed and computing power are necessary. The following article will investigate the usage of GDDR6 in AI and ML applications, as well as current IP trends in this crucial subject.

Architecture of GDDR6

High-speed dynamic random-access memory with high bandwidth requirements is the GDDR6 DRAM. The high-speed interface of the GDDR6 SGRAM is designed for point-to-point communications to a host controller. To accomplish high-speed operation, GDDR6 employs a 16n prefetch architecture and a DDR or QDR interface. The architecture of the technology has two 16-bit wide, completely independent channels.

GDDR6 Controller SGRAM

Figure 1 Block diagram [Source]

The Role of GDDR6 in AI and ML

For AI and ML processes, including the training and inference phases, large-scale data processing is necessary. Avoid AI GPUs (Graphics Processing Units) have evolved into the workhorses of AI and ML systems to make sense of this data. The parallel processing capabilities of GPUs are outstanding, which is crucial for addressing the computational demands of workloads for AI and ML.

Data is a crucial piece of information, high-speed memory is needed to store and retrieve massive volumes of data, and GPU performance depends on data analysis. Since the GDDR5 and GDDR5X chips from earlier generations couldn’t handle data transmission speeds more than 12 Gbps/pin, these applications demand faster memory. Here, GDDR6 memory plays a crucial function. AI and ML performance gains require memory to be maintained, hence High Bandwidth Memory (HBM) and GDDR6 offer best-in-class performance in this situation. The Rambus GDDR6 memory subsystem is designed for performance and power efficiency and was created to meet the high-bandwidth, low-latency requirements of AI and ML. The demand for HBM DRAM has significantly increased for gaming consoles and graphics cards as a result of recent developments in artificial intelligence, virtual reality, deep learning, self-driving cars, etc.

Micron’s GDDR6 Memory

Micron’s industry-leading technology enables the next generation faster, smarter global infrastructures, facilitating artificial intelligence (AI), machine learning, and generative AI for gaming. Micron has launched GDDR6X with NVIDIA GeForce® RTX™ 3090 and GeForce® RTX™ 3080 GPUs due to its high-performance computing, higher frame rates, and increased memory bandwidth.

Micron GDDR6 SGRAMs were designed to work with a 1.35V power supply, making them ideal for graphics cards. The memory controller receives a 32-bit wide data interface from GDDR6 devices. GDDR6 employs two channels that are completely independent of one another. A write or read memory access is 256 bits or 32 bytes wide for each channel. Each 256-bit data packet is converted by a parallel-to-serial converter into 16×16-bit data words that are consecutively broadcast via the 16-bit data bus. Originally designed for graphics processing, GDDR6 is a high-performance memory solution that delivers faster data packet processing. GDDR6 supports an IEEE1149.1-2013 compliant boundary scan. Boundary scan allows testing of interconnect on the PCB during manufacturing using state-of-the-art automatic test pattern generation (ATPG) tools.

GDDR6 2-channel 16n Prefetch Memory Architecture

Figure 2 Source

Rambus GDDR6 Memory Interface Subsystem

The JEDEC GDDR6 JESD250C standard is fully supported by the Rambus GDDR6 interface. The Rambus GDDR6 memory interface subsystem fulfills the high-bandwidth, low-latency needs of AI/ML inference and is built for performance and power economy. It includes a PHY and a digital controller that gives users a full GDDR6 memory subsystem. It provides an industry-leading 24 Gb/s per pin and enables two channels with a combined data width of 32 bits. Each channel supports 16 bits. The Rambus GDDR6 interface has a bandwidth of 96GB/s at 24 Gb/s per pin.

GDDR6 Memory Interface Subsystem Example

Figure 3 [Source]

Application of GDDR6 memory in AI/ML applications

A large variety of AI/ML applications from many industries employ GDDR6 memory. Here are some actual instances of AI/ML applications that make use of GDDR6 memory:

  1. FPGA-based AI applications

Micron in their recent new release focused on the development of High-Performance FPGAs based GDDR6 memory for AI applications built on TSMC 7nm process technology with FPGA from Achronix.

2. GDDR6 memory is ideal for AI/ML inference at the edge where fast storage is essential. It offers better memory bandwidth, system speed, and low latency performance, which makes the system to be used for real-time computing of large amounts of data.

3. Advanced driver assistance systems (ADAS)

ADAS employs GDDR6 memory in visual recognition for processing large amounts of visual data, in multiple sensors for tracking and detection, and for real-time decision-making where a large amount of neutral network-based data is analyzed to reduce accidents and for passenger safety.

4. Cloud Gaming

To provide a smooth gaming experience, cloud gaming uses GDDR6 memory, which is fast memory.

5. Healthcare and Medicine:

GDDR6 is used in faster analysis of medical data in the medical industry implemented with AI algorithms for diagnosis and treatment.

IP Trends in GDDR6 use in machine learning and Artificial intelligence

As the importance of high-speed with low latency memory is increasing, there is a significant growth in the patent filing trends witnessed across the globe. The Highest number of patents granted was in 2022 with 212 patents and the highest number of patent applications filed was ~408 in 2022.

INTEL is a dominant player in the market with ~1107 patent families. So far, it has 2.5 times more patent families than NVIDIA Corp., which comes second with 435 patent families. Micron Technology is the third-largest patent holder in the domain.

Other key players in the domain are SK Hynix, Samsung, and AMD.

Top Applicants for GDDR6 Memory Use

[Source: https://www.lens.org/lens/search/patent/analysis?q=(GDDR6%20memory%20use)]

Following are the trends of publication and their legal status over time:

publication status over time
Legal status over time

[Source: https://www.lens.org/lens/search/patent/analysis?q=(GDDR6%20memory%20use)]

Conclusion

High-speed memory is a hero who goes unnoticed in the quick-paced world of AI and ML, where every millisecond matters. It has stepped up to the plate, providing great bandwidth, low latency, and enormous capacity, making GDDR6 memory an essential part of AI and ML systems. The IP trends for GDDR6 technology indicate continued attempts to enhance memory solutions for these cutting-edge technologies as demand for AI and ML capabilities rises. These developments bode well for future AI and ML developments, which should become much more amazing.

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Automotive

V2X Technology: Revolutionizing Transportation and Our Future

V2X Technology

Technology keeps pushing the limits of innovation in the quickly changing field of transportation. Vehicle-to-Everything (V2X) communication technology is one such ground-breaking development that is transforming how vehicles interact with their environment. V2X refers to a group of communication technologies that allow vehicles to interact with networks, infrastructure (RSU), pedestrians, and other vehicles (V2V, V2P, and V2N).

V2V communication

V2V communication, which involves direct communication between vehicles, is part of V2X technology. Vehicles can increase traffic efficiency, increase road safety, and enable cooperative driving by communicating real-time information. Vehicles can exchange information about their position, velocity, acceleration, and trajectory through V2V communication. Advanced safety features including collision warnings, emergency braking assistance, and cooperative adaptive cruise control are made possible by this information sharing.

V2I communication

Establishing a connection between cars and the surrounding infrastructure, such as traffic lights, roadside sensors, RSU, and road signage, is the main goal of V2I communication. Vehicles can get updates on the state of the roads, traffic light timings, and real-time traffic data through a V2I connection. Informed judgments may be made, routes can be optimized, and driving behavior can be modified as a result. Traffic management systems may also monitor and regulate traffic flow, improve signal timings, and give precedence to emergency vehicles. Additionally, V2I integration is essential for the development of smart cities and intelligent transportation systems.

V2P communication

By enabling vehicles to identify and interact with road users including bicycles and pedestrians, V2P communication seeks to improve pedestrian safety. This variation of V2X technology makes use of sensors, such as cameras and radars, to find pedestrians who are close to the car. Once the pedestrian has been identified, the car can share data with them, giving both of them alerts or cautions. For instance, when a car is near a crossing, it can send out a signal to pedestrians to let them know it is going to stop after spotting them. Increased awareness, fewer accidents involving pedestrians, and safer cohabitation between automobiles and road users are all benefits of V2P communication.

V2N communication

Data is exchanged between cars and external networks, including cloud-based applications, traffic management hubs, and mobility service providers, using V2N communication. Vehicles may get real-time information regarding traffic patterns, weather forecasts, and parking spots thanks to V2N networking. This knowledge provides drivers with useful insights for effective route planning, traffic avoidance, and parking spot location. Furthermore, the V2N connection makes it possible for automakers to remotely install performance upgrades, bug repairs, and new features, assuring the best possible vehicle performance and safety.

V2X technology has multiple benefits, and has a potential impact on our future:

“Traffic Efficiency and Management”

V2X technology is essential for improving traffic management and efficiency. V2X systems help intelligent traffic management systems make wise decisions by gathering real-time data on traffic flow, congestion, and road conditions. Based on the actual traffic demand, traffic lights may be dynamically changed to shorten wait times and improve traffic flow.

“Enabling Autonomous Driving”

Self-driving cars with V2X capabilities may communicate with other vehicles and infrastructure to share information, which enables them to safely and effectively manage challenging traffic situations. Autonomous cars can make educated judgments and respond quickly by receiving real-time data through V2X communication on the state of the roads, traffic patterns, and possible dangers. This innovation speeds up the incorporation of autonomous cars into our transportation infrastructure by improving their dependability and safety.

“Enhancing Road Safety”

Enhancing road safety is one of V2X technology’s main objectives. V2X systems provide cars the ability to interact with one another and their surroundings, allowing them to share useful information that can lower risks and avert accidents. V2V communication, for instance, might warn drivers of impending crashes, abrupt braking, or perilous road conditions. By informing drivers of construction zones, traffic signal timings, and traffic congestion, V2I communication can improve traffic flow and lessen congestion. Additionally, V2P communication makes it possible for cars to recognize and react to vulnerable road users including walkers, cyclists, and others, improving their safety.

“Reduced Fuel Consumption and Emissions”

V2X technology helps optimize fuel economy and lower emissions, especially when paired with autonomous driving features. Vehicles equipped with V2X systems can exchange data on traffic conditions, road gradients, and upcoming traffic signals. This information enables the vehicles to adjust their speed and acceleration patterns efficiently, minimizing unnecessary fuel consumption and emissions.

Some potential disadvantages and challenges associated with V2x

Some potential disadvantages and challenges associated with V2x:

“Infrastructure Deployment”

The installation of communication infrastructure, such as roadside devices, traffic sensors, and network connectivity, is necessary for the implementation of V2X technology. Particularly when it comes to comprehensive coverage throughout a whole area or nation, this may be a pricey and time-consuming operation. Particularly in rural or resource-constrained places, the initial investment and infrastructure maintenance expenses may be problematic.

“Interoperability and Standardization”

V2X technology depends on the creation of standard communication protocols and guidelines to guarantee compatibility between various cars and infrastructure parts. However, because different regulatory frameworks, competing corporate interests, and various regional agendas exist, establishing global standardization can be challenging. The successful use of V2X systems may be constrained by a lack of compatibility, which might impede the efficient flow of information.

“Security risks” The technology involves the transmission of sensitive data, such as location and speed information, between vehicles and infrastructure. This data is vulnerable to cyberattacks, which could compromise the safety and privacy of drivers and passengers. Hackers could potentially gain access to the V2X system and use it to cause accidents or steal personal data. In order to address these security risks, V2X systems will need to be built with robust cybersecurity measures in place. This will require a significant investment in security technologies and protocols, as well as ongoing monitoring and updates to ensure that the system remains secure over time. Additionally, stakeholders will need to develop clear policies and regulations around data privacy and security to ensure that personal data is protected and used only for its intended purposes.

Categories
Automotive Electronics

Ford’s Latest Patent Shows a New Way of Using Augmented Reality (AR) in Cars

Ford’s Latest Patent Shows a New Way of Using AR in Cars

Ford doesn’t have plans to stop offering more helpful tech in the market. Presently, they have thought about it as a superior method for utilizing Augmented Reality (AR) in vehicles. It could seem like a trick for now, however here’s the reason it could come up as truly cool and helpful.

If you drive a lot, you certainly had days when the road was definitely not a safe spot to be because the solar glare was extremely strong at sunrise or sunset. Add a wet road to this situation, and you’ll get a formula for the ideal disaster.

You can attempt to utilize glasses, sun visors, or simply squint your eyes, however, there are minutes when it is truly difficult to see the road properly. Ford wants to assist you with this by giving you improved augmented reality in a future vehicle of the Michigan-based producer.

While Mercedes-Benz made every one of the headlines with AR navigation presently that is now gradually advancing onto more luxury or premium vehicles, the innovation is commonly known since it was utilized in mobile games like Pokemon Go.

As indicated by a recently unveiled United States Patent and Trademark Office (USPTO) documenting, Ford has one more use for AR. The automaker plans to install this system in a car equipped with a head-up display (HUD). The organization refers to it as “User-centric Enhanced Pathway,” or UEP for short.

Ford’s new patented AR system will utilize the HUD, cameras, GPS, and radars to project on the windscreen the way that should be followed by the driver. It’s basically providing you with one more set of virtual eyes. Moreover, it will actually be able to change settings like brightness, color, and contrast all on its own to ensure the individual in the driver’s seat sees what data is being displayed to them.

The Augmented Reality framework will likewise know when it should activate on its own in light of information like the driver’s eye squint status, the speed, the area, or the weather conditions at a specific moment within the day.

This innovation isn’t restricted to vehicles or trucks, as Ford is hoping to introduce it even in vans or public transport like buses. You can observe much more specific insights regarding this new AR framework in the USPTO filing.

You should remember that patents aren’t really an assurance of production. The American carmaker simply needs to ensure its innovation is safeguarded by the law. Given the ongoing environment of things and what the future prognosis says about getting back to normal, it’s difficult to envision Ford betting everything with this at any point in the near future. For now, it’s great that they basically have it.