HBM goes mainstream
Time:2024-12-04
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As AI models grow in size and complexity, they generate and process increasingly large datasets, leading to performance bottlenecks in in-memory systems. These memory-intensive operations put a strain on the memory hierarchy, especially in high-throughput scenarios such as training large neural networks.
We have seen CPU processing power continue to increase, following Moore‘s Law, but memory access speeds have not maintained the same pace. Dedicated AI hardware, while capable of extremely high parallelism, is limited by memory latency and bandwidth. This bottleneck, often referred to as a memory wall, can severely impact overall system performance. To address these challenges and close the memory performance gap, advances in areas such as 3D stacked memory technology, often referred to as High Bandwidth Memory (HBM), are being explored.
HBM utilizes a 3D stacked architecture in which memory chips are vertically stacked and interconnected via silicon through vias (TSVs). The stacked DRAM is connected to the processor chip through an intermediate layer. This reduces the physical distance over which data must travel and allows for higher data rates and lower latency.
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Overall, HBM has the following advantages:
High bandwidth - The use of a wide memory interface bus provides a large amount of bandwidth for data transfers between chips. This is especially useful for parallel processing workloads such as those in AI model training and deep learning.
Smaller form factor - HBM‘s 3D stack design takes up less space than traditional memory configurations. These stacks are then mounted on a silicon or organic intermediary layer next to the processor, resulting in a highly compact memory system.
Low Power Consumption - HBM is also designed to consume less power than traditional memory, especially when delivering high bandwidth. Low power consumption is a key factor in the design of modern computing hardware, especially for AI systems that are typically deployed at scale.
Reduced Latency - HBM offers lower latency than off-chip memory solutions such as DDR and GDDR. With recent investments in advanced packaging technologies such as 2.5D intermediate layers and 3D stacking, it enables more compact SoC designs for heterogeneous computing.
For applications where performance and bandwidth are critical, HBM offers significant advantages and remains one of the most viable solutions despite its high cost and complexity. As computational workloads evolve due to the explosive growth of AI and Big Data, new ways of managing and accessing memory are critical to overcoming memory bottlenecks.
The challenges that come with it, however, are obvious.
And as the complexity of AI continues to grow, the role of HBM in unlocking the full potential of next-generation AI hardware will become increasingly important. As it evolves, next-generation HBM4 and HBM4E technologies will further address the needs of AI workloads by doubling the interface width to 2048 bits.
HBM Implementation Challenges
Because implementing a 2.5D System-in-Package (SiP) with High Bandwidth Memory (HBM) is a complex process that involves architecture definition, designing highly reliable intermediary layer channels, and performing robust testing of the entire data path, including system-level verification.
Overall, HBM presents several challenges:
Manufacturing Complexity - HBMs are built using 3D stacked architectures, and the precision required to fabricate TSVs and align multilayer memory chips is much higher than traditional memory. In addition, HBMs are typically mounted on silicon intermediary layers or organic intermediary layers, which provide high-speed communication between the memory stack and the processor. This requires advanced lithography and precise chip placement, which adds to the overall complexity of manufacturing.
Thermal Management - Due to the stacking nature of HBM, multiple DRAM chips are placed on top of each other and the heat generated by the memory chips accumulates in the stack. This poses a significant thermal challenge. Advanced cooling methods such as liquid cooling, thermal interface materials (TIM) and integrated heat sinks are often required to mitigate thermal throttling.
Total Cost of Ownership - Achieving high yields can be very challenging due to the advanced manufacturing techniques required for 2.5D intermediate layers and 3D stacking technologies. Even a single defect in any stacked chip or interconnect can cause the entire HBM stack to fail, reducing overall manufacturing yields and increasing costs.
In terms of implementation, the following aspects need to be taken into account:
First, during high-level design and architecture planning, it is important to determine the necessary bandwidth, latency, and power requirements for planning the overall system architecture. Monolithic chips can also be broken down into smaller specialized modules (called chiplets) to handle specific functions within the system. This approach can provide enhanced design flexibility, power efficiency, yield, and overall scalability.
Coming to the intermediary layer design, since the intermediary layer can be silicon or organic material and supports multiple metal layers to handle the high density wiring between the HBM stack and the compute chip. It‘s worth mentioning because HBM4 will build on the improvements made in HBM3E and is designed to further increase data rates, energy efficiency and memory density. Since the interface width is doubled (to 2048 bits) but the HBM4 memory shoreline remains unchanged from HBM3E, the main challenge is to manage the denser I/O cabling in the PHY and intermediary layers. The layout should ensure careful signal routing, power distribution and grounding to minimize crosstalk and loss through the channel to meet HBM4E specifications.
Going to the SI and PI analysis, to prevent signal attenuation at HBM4E data rates, we need to perform techniques such as impedance matching, shielding, and take steps to ensure that crosstalk between adjacent alignments is minimized. Characterization of the intermediate layer includes Insertion Loss (IL), Reflection Loss (RL), Power Sum Crosstalk (PSXT) and Insertion Loss to Crosstalk Ratio (ICR) to characterize the channel and ensure that we are meeting the requirements of the next generation of HBM4E technology.
Additionally, the power supply network needs to be carefully planned to identify decoupling capacitors, low impedance paths and dedicated power layers for critical sensitive signals. Noise contributions from all components such as motherboards, packages, intermediary layers and wafers need to be considered when determining the target impedance of the supply network.
Finally, extensive SI-PI testing ensures that the HBM package meets jitter and power specifications. Decomposing jitter due to the intermediary layer into ISI, crosstalk, and rise-fall time degradation helps to identify the major channel parameters that affect EYE closure and contributes to better layout and I/O architecture optimization.
System-level testing of all components in the datapath is critical to ensure that the assembled package meets the performance specifications defined during the design phase. A comprehensive test suite that includes DFT-enabled designs is also critical for early diagnostics to enable high yields.
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Elan Tsao