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Computer Science

Enhancing AI Accelerators with HBM3: Overcoming Memory Bottlenecks in the Age of Artificial Intelligence

High Bandwidth Memory 3 (HBM3): Overcoming Memory Bottlenecks in AI Accelerators With the rise of generative AI models that can produce original text, picture, video, and audio material, artificial intelligence (AI) has made major strides in recent years. These models, like large language models (LLMs), were trained on enormous quantities of data and need a lot of processing power to function properly. However, because of their high cost and processing requirements, AI accelerators now require more effective memory solutions. High Bandwidth Memory, a memory standard that has various benefits over earlier memory technologies, is one such approach. How HBM is relevant to AI accelerators? Constant memory constraints have grown problematic in a number of fields over the past few decades, including embedded technology, artificial intelligence, and the quick growth of generative AI. Since external memory interfaces have such a high demand for bandwidth, several programs have had trouble keeping up. An ASIC (application-specific integrated circuit) often connects with external memory, frequently DDR memory, through a printed circuit board with constrained interface capabilities. The interface with four channels only offers about 60 MB/s of bandwidth even with DDR4 memory. While DDR5 memory has improved in this area, the improvement in bandwidth is still just marginal and cannot keep up with the continuously expanding application needs. However, a shorter link, more channels, and higher memory bandwidth become practical when we take the possibility of high memory bandwidth solutions into account. This makes it possible to have more stacks on each PCB, which would greatly enhance bandwidth. Significant advancements in high memory bandwidth have been made to suit the demands of many applications, notably those demanding complex AI and machine learning models. Latest generation of High Bandwidth Memory The most recent high bandwidth memory standard is HBM3, which is a memory specification for 3D stacked SDRAM that was made available by JEDEC in January 2022. With support for greater densities, faster operation, more banks, enhanced reliability, availability, and serviceability (RAS) features, a lower power interface, and a redesigned clocking architecture, it provides substantial advancements over the previous HBM2E standard (JESD235D). [Source: HBM3 Standard [JEDEC JESD238A] Page 16 of 270] P.S. You can refer to HBM3 Standard [JEDEC JESD238A]: https://www.jedec.org/sites/default/files/docs/JESD238A.pdf for further studies. How does HBM3 address memory bottlenecks in AI accelerators? HBM3 is intended to offer great bandwidth while consuming little energy, making it perfect for AI tasks that need quick and effective data access. HBM3 has a number of significant enhancements over earlier memory standards, including: Increased bandwidth Since HBM3 has a substantially larger bandwidth than its forerunners, data may be sent between the memory and the GPU or CPU more quickly. For AI tasks that require processing massive volumes of data in real-time, this additional bandwidth is essential. Lower power consumption Since HBM3 is intended to be more power-efficient than earlier memory technologies, it will enable AI accelerators to use less energy overall. This is crucial because it may result in considerable cost savings and environmental advantages for data centers that host large-scale AI hardware. Higher memory capacity Greater memory capacities supported by HBM3 enable AI accelerators to store and analyze more data concurrently. This is crucial for difficult AI jobs that need access to a lot of data, such as computer vision or natural language processing. Improved thermal performance AI accelerators are less likely to overheat because to elements in the architecture of HBM3 that aid in heat dissipation. Particularly during demanding AI workloads, this is essential for preserving the system's performance and dependability. Compatibility with existing systems Manufacturers of AI accelerators will find it simpler to implement the new technology because HBM3 is designed to be backward-compatible with earlier HBM iterations without making substantial changes to their current systems. This guarantees an easy switch to HBM3 and makes it possible for quicker integration into the AI ecosystem. In a word, HBM3 offers enhanced bandwidth, reduced power consumption, better memory capacity, improved thermal performance, and compatibility with current systems, making it a suitable memory choice for AI accelerators. HBM3 will play a significant role in overcoming memory constraints and allowing more effective and potent AI systems as AI workloads continue to increase in complexity and size. Intellectual property trends for HBM3 in AI Accelerators HBM3 in AI Accelerators is witnessing rapid growth in patent filing trends across the globe. Over the past few years, the number of patent applications almost getting doubled every two years. MICRON is a dominant player in the market with 50% patents. It now holds twice as many patents as Samsung and SK Hynix combined. Performance, capacity, and power efficiency in today's AI data centers are three areas where Micron's HBM3 Gen2 "breaks new records." It is obvious that the goal is to enable faster infrastructure utilization for AI inference, lower training periods for big language models like GPT-4, and better total cost of ownership (TCO). Other key players who have filed for patents in High bandwidth memory technology with are Intel, Qualcomm, Fujitsu etc. [Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence] Following are the trends of publication and their legal status over time: [Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence] These Top companies own around 60% of total patents related to UFS. The below diagram shows these companies have built strong IPMoats in US jurisdiction. [Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence] Conclusion In summary, compared to earlier memory standards, HBM3 provides larger storage capacity, better bandwidth, reduced power consumption, and improved signal integrity. HBM3 is essential for overcoming memory limitations in the context of AI accelerators and allowing more effective and high-performance AI applications. HBM3 will probably become a typical component in the next AI accelerator designs as the need for AI and ML continues to rise, spurring even more improvements in AI technology. Meta Data The performance of AI accelerators will be improved by the cutting-edge memory technology HBM3, which provides unparalleled data speed and efficiency.

High Bandwidth Memory 3 (HBM3): Overcoming Memory Bottlenecks in AI Accelerators

With the rise of generative AI models that can produce original text, picture, video, and audio material, artificial intelligence (AI) has made major strides in recent years. These models, like large language models (LLMs), were trained on enormous quantities of data and need a lot of processing power to function properly. However, because of their high cost and processing requirements, AI accelerators now require more effective memory solutions. High Bandwidth Memory, a memory standard that has various benefits over earlier memory technologies, is one such approach.        

How HBM is relevant to AI accelerators?

Constant memory constraints have grown problematic in a number of fields over the past few decades, including embedded technology, artificial intelligence, and the quick growth of generative AI. Since external memory interfaces have such a high demand for bandwidth, several programs have had trouble keeping up. An ASIC (application-specific integrated circuit) often connects with external memory, frequently DDR memory, through a printed circuit board with constrained interface capabilities. The interface with four channels only offers about 60 MB/s of bandwidth even with DDR4 memory. While DDR5 memory has improved in this area, the improvement in bandwidth is still just marginal and cannot keep up with the continuously expanding application needs.

However, a shorter link, more channels, and higher memory bandwidth become practical when we take the possibility of high memory bandwidth solutions into account. This makes it possible to have more stacks on each PCB, which would greatly enhance bandwidth. Significant advancements in high memory bandwidth have been made to suit the demands of many applications, notably those demanding complex AI and machine learning models.

The latest generation of High Bandwidth Memory

The most recent high bandwidth memory standard is HBM3, which is a memory specification for 3D stacked SDRAM that was made available by JEDEC in January 2022. With support for greater densities, faster operation, more banks, enhanced reliability, availability, and serviceability (RAS) features, a lower power interface, and a redesigned clocking architecture, it provides substantial advancements over the previous HBM2E standard (JESD235D). 

General Overview of DRAM Die Stack with Channels

[Source: HBM3 Standard [JEDEC JESD238A] Page 16 of 270]

P.S. You can refer to HBM3 Standard [JEDEC JESD238A]: https://www.jedec.org/sites/default/files/docs/JESD238A.pdf for further studies.   

How does HBM3 address memory bottlenecks in AI accelerators?

HBM3 is intended to offer great bandwidth while consuming little energy, making it perfect for AI tasks that need quick and effective data access. HBM3 has a number of significant enhancements over earlier memory standards, including:

Increased bandwidth

Since HBM3 has a substantially larger bandwidth than its forerunners, data may be sent between the memory and the GPU or CPU more quickly. For AI tasks that require processing massive volumes of data in real time, this additional bandwidth is essential.

Lower power consumption

Since HBM3 is intended to be more power-efficient than earlier memory technologies, it will enable AI accelerators to use less energy overall. This is crucial because it may result in considerable cost savings and environmental advantages for data centers that host large-scale AI hardware.

Higher memory capacity

Greater memory capacities supported by HBM3 enable AI accelerators to store and analyze more data concurrently. This is crucial for difficult AI jobs that need access to a lot of data, such as computer vision or natural language processing.

Improved thermal performance

AI accelerators are less likely to overheat because to elements in the architecture of HBM3 that aid in heat dissipation. Particularly during demanding AI workloads, this is essential for preserving the system’s performance and dependability.

Compatibility with existing systems

Manufacturers of AI accelerators will find it simpler to implement the new technology because HBM3 is designed to be backward-compatible with earlier HBM iterations without making substantial changes to their current systems. This guarantees an easy switch to HBM3 and makes it possible for quicker integration into the AI ecosystem.

In a word, HBM3 offers enhanced bandwidth, reduced power consumption, better memory capacity, improved thermal performance, and compatibility with current systems, making it a suitable memory choice for AI accelerators. HBM3 will play a significant role in overcoming memory constraints and allowing more effective and potent AI systems as AI workloads continue to increase in complexity and size.

Intellectual property trends for HBM3 in AI Accelerators

HBM3 in AI Accelerators is witnessing rapid growth in patent filing trends across the globe. Over the past few years, the number of patent applications almost getting doubled every two years.    

MICRON is a dominant player in the market with 50% patents. It now holds twice as many patents as Samsung and SK Hynix combined. Performance, capacity, and power efficiency in today’s AI data centers are three areas where Micron’s HBM3 Gen2 “breaks new records.” It is obvious that the goal is to enable faster infrastructure utilization for AI inference, lower training periods for big language models like GPT-4, and better total cost of ownership (TCO).       

Other key players who have filed for patents in High bandwidth memory technology with are Intel, Qualcomm, Fujitsu etc.   

key players who have filed for patents in High bandwidth memory

[Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence]  

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

Legal status for patent applications and documents

[Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence]

These Top companies own around 60% of total patents related to UFS. The below diagram shows these companies have built strong IPMoats in US jurisdiction.  

IPMoats in US jurisdiction

[Source: https://www.lens.org/lens/search/patent/list?q=stacked%20memory%20%2B%20artificial%20intelligence]

Conclusion

In summary, compared to earlier memory standards, HBM3 provides larger storage capacity, better bandwidth, reduced power consumption, and improved signal integrity. HBM3 is essential for overcoming memory limitations in the context of AI accelerators and allowing more effective and high-performance AI applications. HBM3 will probably become a typical component in the next AI accelerator designs as the need for AI and ML continues to rise, spurring even more improvements in AI technology.    

Meta Data

The performance of AI accelerators will be improved by the cutting-edge memory technology HBM3, which provides unparalleled data speed and efficiency.

Categories
Electronics

Understanding Hidden Markov Model in Natural Language – Decoding Amazon Alexa

Decoding Amazon Alexa's: The Magic Behind Natural Language Processing (AI) Integration

Alexa is a cloud-based software program that acts as a voice-controlled virtual personal assistant. Alexa works by listening for voice commands, translating them into text, interpreting the text to carry out corresponding functions, and delivering results in the form of audio, video, or device/accessory triggers.

Hidden Markov Models (HMMs) are a type of probability model that can be used in Natural Language Understanding (NLU) to help programs come to the most likely decision based on both previous decisions and observations.

Machine learning plays a critical role in improving Alexa’s ability to understand and respond to voice commands over time.

Alexa has three main parts: Wake word, Invocation name, and Utterance. Here is a breakdown of each part:

  • Wake word: This is the word that users say to activate Alexa. By default, the wake word is “Alexa,” but users can change it to “Echo,” “Amazon,” or “Computer.
  • Invocation name: This is the unique name that identifies a custom skill. Users can invoke a custom skill by saying the wake word followed by the invocation name. The invocation name must not contain the wake words “Alexa,” “Amazon,” “Echo,” or the words “skill” or “app.
  • Utterance: This is the spoken phrase that users say to interact with Alexa. Users can include additional words around their utterances, and Alexa will try to understand the intent behind the words.
Natural Language Processing (NLP)

What is NLP?

Natural Language Processing (NLP) is a key component of Alexa’s functionality. NLP is a branch of computer science that involves the analysis of human language in speech and text. It is the technology that allows machines to understand and interact with human speech, but is not limited to voice interactions. NLP is the reader that takes the language created by Natural Language Generation (NLG) and consumes it. Advances in NLP technology have allowed dramatic growth in intelligent personal assistants such as Alexa.

Alexa uses NLP to process requests or commands through a machine learning technique. When a user speaks to Alexa, the audio is sent to Amazon’s servers to be analysed more efficiently. To convert the audio into text, Alexa analyses characteristics of the user’s speech such as frequency and pitch to give feature values. The Alexa Voice Service then processes the response and identifies the user’s intent, making a web service request to a third-party server if needed.

In summary, NLP is the technology that allows Alexa to understand and interact with human speech. It is used to process requests or commands through a machine learning technique, and NLU is a key component of Alexa’s functionality that allows it to infer what a user is asking for when they ask a question in a variety of ways.

Hidden Markov Model (NLU Example) 

Hidden Markov Model (NLU Example) 

HMMs are used in Alexa’s NLU to help understand the meaning behind the words spoken by the user. Here is an example of how HMMs can be used in Alexa’s NLU:

  1. The user says “Alexa, play some music.”
  3. The audio is sent to Amazon’s servers to be analyzed more efficiently.
  4. The audio is converted into text using speech-to-text conversion.
  5. The text is analyzed using an HMM to determine the user’s intent. The HMM takes into account the previous decisions made by the user, such as previous music requests, as well as the current observation, which is the user’s request to play music.
  6. Alexa identifies the user’s intent as “play music” and performs the requested action.

Conclusion

In summary, Alexa’s NLP architecture involves converting the user’s spoken words into text, processing the text to identify the user’s intent, and performing complex operations such NLU using the Alexa Voice Service.

Categories
Electronics

Wi-Fi Offloading: Boosting Connectivity, Saving Costs, and Easing Network Congestion

Connectivity, Saving Costs, and Easing Network Congestion

In an increasingly connected world, where our dependency on mobile devices and data use is rising, the demand for fast and dependable internet access is at an all-time high. But the study found that mobile networks frequently fail to keep up with increased demand, resulting in slower speeds, crowded networks, and disgruntled consumers.

To overcome this issue, WIFI offloading has emerged as a possible alternative. In this blog, we will look at the notion of WIFI offloading, its benefits, and how it works.

WIFI Offloading Understanding:

Wi-Fi offloading is the practice of using Wi-Fi hotspots to keep mobile devices connected. This can be done manually or by logging into a home or public Wi-Fi network. When a device moves from a cellular connection to Wi-Fi or small cell connectivity, such as when mobile traffic is offloaded to public hotspots.

WiFi offloading, or mobile data offloading, diverts cellular network traffic to WiFi networks, improving connectivity and reducing strain on mobile networks. This blog explores the benefits and mechanics of WiFi offloading.

Benefits of WiFi Offloading:

WiFi offloading offers several advantages.

  • It enhances connectivity by leveraging faster and more reliable WiFi networks, especially in areas with weak cellular signals.
  • It leads to cost savings by reducing mobile data consumption, as WiFi usage doesn’t count towards cellular data caps.
  • It reduces network congestion, improving overall network performance during peak usage. Finally, WiFi offloading can extend battery life on mobile devices, as transmitting data over WiFi is more energy-efficient.

How WiFi Offloading Works:

Mobile devices use network selection algorithms to determine the best connection when both cellular and WiFi options are available. Seamless handover ensures uninterrupted connectivity, as devices automatically switch from cellular to WiFi when a connection is available. Authentication protocols and security measures protect data while connected to WiFi networks.

If we speak in technical terms, WiFi offloading refers to a type of handover between a non-WiFi network and a WiFi network.

Mobile data offloading

Figure: 1. Mobile data offloading

Source: https://www.researchgate.net/figure/Description-of-Mobile-Data-Offloading_fig2_326030064

Let us look into Figure 1. This explains the offloading procedure, so assume that at time t, a mobile node (MN) seeks to initiate a data transfer session. While the cellular network is always presumed to be available, the WiFi network is only accessible when the MN is close enough to the WiFi coverage. The offloading technique employs a network selection algorithm based on Received Signal Strength (RSS).

Received Signal Strength: The Received Signal Strength (RSS) informs the receiver about the strength of the received signal, which represents the power of the signal at the receiving end.

Received Signal Strength (RSS) BLE Transmitter and Receiver

Source: https://pcng.medium.com/received-signal-strength-rss-8a306b12d520

Smartphone operating systems like Android, offer convenient access to the Received Signal Strength (RSS) value when the smartphone receives a Bluetooth Low Energy (BLE) packet. By utilizing the Android. Bluetooth SDK, we can retrieve this value through the RSSI variable.

The RSS values can provide valuable insights about the BLE transmitter. One practical application is estimating the distance between our smartphone and the BLE transmitter. We can collect the RSS values at various distances and employ curve-fitting methods to create a ranging model. Alternatively, a simple machine learning approach, such as linear regression, can be applied to learn the ranging model.

Conclusion:

WiFi offloading optimizes connectivity by diverting data traffic to WiFi networks. It offers benefits such as enhanced connectivity, cost savings, reduced network congestion, and improved battery life. As data demands increase, WiFi offloading proves valuable in providing seamless connectivity and addressing network limitations. WiFi offloading works by using network selection algorithms to determine the best connection and ensure seamless handover between cellular and WiFi networks. The Received Signal Strength (RSS) plays a crucial role in this process, providing information about the strength of the received signal.