Redefining XPU Memory for AI Data Centers Through Custom HBM4 – Part 2
Part 2: HBM implementation challenges
This is the second in a three-part series from Alphawave Semi on HBM4 and gives insights into HBM implementation challenges. Click here for part 1, for an overview on HBM, and in part 3, we will introduce details of a custom HBM implementation.
Implementing a 2.5D System-in-Package (SiP) with High Bandwidth Memory (HBM) is a complex process that spans across architecture definition, designing a highly reliable Interposer channel and robust testing of the entire data path including system level validation. Here is a breakdown of the key elements and considerations involved in implementing a 2.5D HBM design.
Advanced Design and Architecture Planning
Determining the necessary bandwidth, latency and power requirements are important to plan overall system architecture. A monolithic chip can also be disaggregated to 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.
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Related Semiconductor IP
- TSMC CLN3FFP HBM4 PHY
- HBM4 Memory Controller
- HBM4E PHY and controller
- HBM4/3E Combo PHY & Controller
Related Blogs
- Redefining XPU Memory for AI Data Centers Through Custom HBM4 – Part 1
- Redefining XPU Memory for AI Data Centers Through Custom HBM4 – Part 3
- LPDDR6: A New Standard and Memory Choice for AI Data Center Applications
- HBM4 Boosts Memory Performance for AI Training