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Published- July 2023 Number Of Pages -101
In comparison to the current HBM2E standard (JESD235D), the HBM3 standard offers a number of feature improvements, including support for higher densities, faster operation, a greater number of banks, improved Reliability, Availability, and Serviceability (RAS) capabilities, a lower power interface, and a new clocking architecture.
Soon, HPC applications like AI, graphics, networking, and possibly even automobiles will use HBM3 memory.
HBM2E has a maximum device density of 16 Gb, which can be implemented in a 12-high stack for a 24 GB total density. Although the standard permits them, 12-high HBM2E stacks have yet to appear on the market.
The HBM3 standard allows for devices with up to 32 Gb of density and a maximum stack height of 16 for a total storage capacity of 64 GB.
Google Cloud and Nvidia
Earlier this year (2023), Google Cloud announced the private preview launch of its H100-powered A3 GPU virtual machines, which combines Nvidia’s chips with Google’s custom-designed 200 Gpbs Infrastructure Processing Units (IPUs). At its Cloud Next conference, Google announced that it will launch the A3 into general availability next month.
Micron
Micron is the first to sample what it terms second-gen HBM3 memory, outstripping rivals like SK hy nix and Samsung, which could be an advantage given the current AI arms race that finds vendors scrambling for the fastest and most capacious memory possible to power memory-hungry AI accelerators
We can also see an ‘HBM Next’ listed on Micron’s roadmap, which could be HBM4. This next-gen memory will deliver 2+ TB/s of throughput and up to 64GB of capacity when it arrives ~2026
Samsung Electronics
SK Hynix
The Global HBM3 Market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
The memory industry as a whole is already preparing for the forthcoming launch of the HBM3 generation of High Bandwidth Memory, even though the formal specification has not yet been certified by JEDEC.
This morning, SK Hynix is announcing that it has completed development of its HBM3 memory technology, becoming the first memory provider to do so, after statements from controller IP vendors like Synopsys and Rambus.
The stage is being set for formal ratification of the standard and eventually for the release of HBM3-equipped devices with controller IP and the memory itself nearing or at completion. Even with its high production costs, HBM commands significant price premiums, so memory vendors are also competitive to be first.
Going into the specifics, SK Hynix claims that the HBM3 memory they are developing would have a pin speed of up to 6.4Gbps. This would be 78 percent quicker than the company’s off-spec 3.6Gbps/pin HBM2E SKUs and twice as fast as today’s HBM2E, which officially tops out at 3.2Gbps/pin.
ML AI servers are equipped with an average of four or eight high-end graphics cards and two mainstream x86 server CPUs. These servers are primarily used by top US cloud services providers such as Google, AWS, Meta, and Microsoft. Companies such as Synopsys & Rambus have developed silicon-proven HBM3 sub-systems, controllers and physical interfaces.
These sub-systems supports up to 1TB/s bandwidth per stack and supports up to 16 DRAM stack. HBM3 comprises a stack of multiple DRAM devices across several independent interfaces. As per JEDEC, in HBM3, each DRAM stack can support up to 16 channels compared to 8 channels in HBM. Which will be the key factor in the upcoming future
Reduced channel and pseudo channel size makes for a better-performing system to have more smaller channels, which is what we’ve seen happen with HBM. From HBM2e to HBM3, they dropped the channel and pseudo channel size very specifically to address the kind of need from the market.
The 8-high 24GB HBM3 Gen2 memory performance, capacity, and power efficiency parameters for artificial intelligence (AI) data centers are broken by Micron’s HBM3 Gen2 product. These Micron advancements result in faster infrastructure utilization for AI inference, shorter training cycles for advanced language models like GPT-4, and better overall cost of ownership.
The performance-to-power ratio and pin speed enhancements of Micron’s HBM3 Gen2 are essential for addressing the high power requirements of today’s AI data centers.
Because of innovations made by Micron, such as tripling the number of through-silicon vias (TSVs) compared to competing HBM3 solutions, reducing thermal impedance by five times more metal density, and designing an energy-efficient data path, the improved power efficiency is now attainable.
The Micron HBM3 Gen2 solution responds to the growing demand for multimodal, multi trillion-parameter AI models in the realm of generative AI. Additionally, the Micron offering enables a large increase in daily requests, enhancing the effectiveness of trained models. The best-in-class performance per watt of Micron HBM3 Gen2 memory enables real cost savings for contemporary AI data centers.
With a focus on providing our clients and the industry with superior AI and high-performance computing solutions, Micron’s HBM3 Gen2 technology was created. The simplicity of integrating our HBM3 Gen2 product into the platforms of our customers has been a key factor.
With a completely programmable Memory Built-In Self Test (MBIST) that operates at full specification pin speed, we are better positioned to test with our customers, collaborate more effectively, and bring products to market faster.
Accelerated computing, which benefits from HBM’s high bandwidth and energy efficiency, is the foundation of generative AI. By utilizing its global engineering organization, Micron created this ground-breaking product. Design and process development took place in the United States, memory production in Japan, and innovative packaging in Taiwan.
The leading manufacturer of memory chips worldwide, Samsung Electronics, is prepared to supply Advanced Micro Devices Inc. (AMD), a fabless semiconductor designer based in the US, with its high bandwidth memory (HBM) chips and turnkey packaging services.
AMD recently conducted a quality test on Samsung’s HBM3, a fourth-generation HBM chip model, and packaging services. AMD intends to use the chips and services for its Instinct MI300X accelerators.
AMD is growing its business in artificial intelligence accelerators, which combine chip packaging and machine learning methods to analyze a lot of data quickly. AMD is an expert in creating central processing units (CPU) for servers.The US manufacturer will introduce the Instinct MI300X, which combines a CPU, GPU, and HBM3.
Samsung can offer cutting-edge packaging solutions along with HBM goods.AMD had originally planned to use the packaging services of Taiwan Semiconductor Manufacturing Company (TSMC), but it has now changed its mind because the US company’s requirement for the advanced packaging that the Taiwanese foundry giant could not fulfill.
The HBM3 standard enables devices with up to 32 Gb of density and up to 16-high stack for a total of 64 GB storage – almost a 3x growth from HBM2E which have an upper limit of 16 Gb devices that can be implemented in a 12-high stack for a total density of 24 GB.
Less power is required to drive signals back and forth between processing elements and DRAM, In addition to increasing capacity and speed, the improvements in energy efficiency are noteworthy. With HBM3, the core voltage is 1.1V, compared to HBM2E’s 1.2V core voltage. HBM3 also reduces the I/O signaling to 400mV versus 1.2V for HBM2E. There will be further improvements in future generations, as well
S.K Hynix, To improve bandwidth, which is a key performance indicator of HBM, S.K. Hynix is developing a wide range of design technologies including data path optimization, machine learning-based signal line optimization, PVT-aware timing optimization technology, and new process technologies. The base die differs from a typical DRAM process in that it lacks a cell, and by leveraging this characteristic, HBM-optimized process technology is being developed as well as advanced package technologies for 3D stacks
