While both of these types of memory are great for AI, they have their own distinct advantages. GDDR is the ideal choice for systems requiring standard manufacturing processes and cannot absorb the extra costs associated with HBM2E. Xilinx Versal HBM series are a great example of a system that can benefit from HBM2E DRAM. Here are some of the advantages of each type.
High-bandwidth memory
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As the deployment of AI/ML solutions expands from data centers to edge networks and IoT devices, the need for high-bandwidth memory becomes more critical. These high-speed devices are essential for inferencing AI/ML models. To support such applications, high-bandwidth memory must be highly efficient and low-latency. However, high-bandwidth memory is also more expensive than other memory types, and this becomes a problem if the memory is not fast enough.
The combination of high-bandwidth memory and processing-in-memory (PIM) technology can solve the performance and energy challenges that AI applications pose. High-bandwidth memory (HBM) is used in AI-centric systems, allowing them to process large volumes of data while minimizing data movement. High-bandwidth memory is ideal for AI computing, and Samsung is one of the world’s leading providers of advanced memory solutions.
High-bandwidth memory provides superior performance and power efficiency for AI/ML applications. GDDR6 can also help reduce costs and implementation complexity by leveraging existing infrastructure and ensuring signal integrity at high data rates. In addition to offering superior performance and power efficiency, GDDR6 also retains a familiar implementation approach. Increasing the speed of memory in AI/ML applications is essential for the future of the industry.
The current state of the technology is inconclusive, but HBM is essential for AI applications. While HBM is still expensive, its increasing capabilities make it an ideal choice for AI and ML training. With the emergence of several high-speed processors and HBM, the AI world is about to become exponentially more advanced. This is the perfect time to acquire this memory technology. However, there is a limited supply of HBM, so you should be patient in your search for an affordable memory solution.
Xilinx Versal HBM series
The Xilinx Versal HBM and GDDR6 memories for AI are the latest addition to the company’s Versal portfolio. These memory modules combine a heterogeneous accelerator and memory adjacent to the compute to help solve compute bottlenecks. According to Mike Thompson, senior product line manager for Xilinx and Virtex, the high-bandwidth memory modules can enable the development of AI systems by enabling more precise machine learning.
The Versal HBM series is based on the same foundation as the Versal Premium. It is expected to be available in the first half of 2021 and updated in 2022. Xilinx is aggressively introducing new products and leveraging state-of-the-art technology to create complete product lines. With this, Versal HBM and GDDR6 memories for AI are an important addition to Versal Premium chip families.
The Xilinx Versal HBM product line targets large-scale data center applications. It also has on-chip memory interfaces, network interfaces, and programmable processing engines to make them ideal for the needs of AI. The Versal HBM series is a great choice for AI applications, especially in high-performance data-center applications. And after introducing Versal HBM, the company plans to release a new GDDR6 memory series.
The Versal HBM series is a memory-intensive memory solution that offers twice the logic density of the previous generation. The Versal HBM series also features adaptive compute and secure connectivity. Xilinx Versal HBM memory modules are compatible with the Vitis AI software platform. By leveraging a programmable memory platform, data scientists can easily design and deploy AI systems on Xilinx Versal HBM series and GDDR6 memories for AI.
HBM2e DRAM
The performance per watt is the primary difference between GDDR6 and HBM2E, with the latter providing superior performance for AI applications. HBM2e is more efficient than GDDR6, offering double the performance per watt. HBM2e is directly connected to the processing element via an interposer, while GDDR6 requires functionality to be transferred from chip to chip.
Both technologies have their merits. HBM has better performance and power efficiency for AI/ML, while GDDR6 is cheaper and more flexible than HBM. Both are based on proven manufacturing processes. GDDR6 is suitable for a wide range of edge networks and IoT end-point devices, making it a viable choice for AI/ML. This article compares the two types of memory and outlines the advantages and disadvantages of each type.
GDDR6 has many benefits for AI applications, but HBM2E has a competitive edge over GDDR6 when it comes to power and board space. HBM2E provides superior performance for AI applications, but it has higher implementation and manufacturing costs. HBM2E has better performance, but lower power means less heat, which is one of the top operating costs of data centers.
HBM2E is a high-bandwidth memory with high-speed data transfer. GDDR6 has higher bandwidth. While HBM2 is superior for AI applications, GDDR6 has an edge for GPU-intensive applications. Both memory types are suitable for AI and deep learning. If your AI applications require high-performance, HBM2E is the better choice.
Cost
Both GDDR6 and HBM2E are fast, but GDDR6 is more expensive than HBM2. The former has higher data rates per pin, but fewer pins overall. The latter has a larger PHY area, requiring more circuitry and more power. However, HBM2E’s price is still more affordable. So which one is better for AI?
While both HBM2E and GDDR6 offer superior performance for AI training, the latter’s higher implementation costs can be offset by lower power consumption and board space savings. Additionally, the former’s compact architecture provides real benefits in data centers, where reduced heat loads translate to lower operating costs. HBM2E is a successor to HBM2, which was implemented in the second-generation Google TPU and NVIDIA’s Tesla A100.
GDDR6 is the most common memory type used in deep learning applications. GDDR6 is much more expensive than HBM2e, but it provides twice as many TOPS/W, while HBM2e has lower energy consumption. The higher cost of HBM2E is primarily due to the more complicated and costly process of manufacturing it. The downside to this type of technology is that it is not widely available.
Graphcore’s second-generation intelligence processing unit (IPU) uses 896 MiB of on-chip SRAM rather than the higher bandwidth required to offload DRAM. It also uses low-bandwidth remote DRAM that is attached to the host processor. Mid-sized AI models are typically spread out across a cluster of IPUs that use SRAM. It seems like the primary reason why Graphcore has rejected HBM and moved to GDDR6.
Power
If you’ve ever wanted to make an artificial intelligence system, you might be interested in the power of GDDR6 and HBM2E. Both offer similar bandwidth and capacity, but use half the power and have double the TOPS/W. However, while both are proven memory solutions, they have some inherent design considerations. Because they’re faster, GDDR6 is also more expensive.
HBM2E is a 2.5dB-based memory, while GDDR6 is a commodity. However, it has some distinct advantages over GDDR6. HBM2e has a more complex architecture than GDDR6 and requires a slightly older 65nm process. The two types of memory offer similar speeds and bandwidth, but the cost of the former is significantly higher.
Unlike GDDR6, HBM2E is much more power efficient, with less than half the power of GDDR6. Each chip is also more energy-efficient than GDDR6, which means HBM2E memory systems can be used in hyperscale data centers. Hyperscale data centers can use 100 megawatts of power, so power management and heat are mission-critical.
The AI/ML revolution is just beginning, and the demand for more computing power is only increasing. In order to keep up with the demand, new hardware and software must be introduced to support this rapidly growing industry. GDDR6 and HBM2E memory technologies are the ideal solution. In addition, both are extremely power-efficient and will provide top-tier performance. The memory industry is working hard to keep up with these demands, and they’ll continue to innovate to meet them.
