Advanced AI workloads, particularly large language models (LLMs), impose extreme demands on memory capacity, bandwidth, and data movement efficiency. Traditional von Neumann architectures suffer from the “memory wall,” where data transfer between memory and compute units dominates latency and power consumption. Memory-centric approaches—such as processing-in-memory (PIM) and compute-in-memory (CiM) solutions from SK hynix and Samsung—address these bottlenecks by embedding computation directly within memory arrays. Effective implementation requires specialized PCB design, material selection, stack-up optimization, and thermal management to support high-speed interfaces like HBM, GDDR6, CXL, and RDMA while maintaining signal integrity and reliability.
Memory Bandwidth and Compute Demands in Transformer-Based Models
Large Transformer models rely heavily on matrix-vector multiplications (GEMV) during inference. These operations are often memory-bound, with GEMV accounting for 60–80% of generation latency in many workloads. Memory capacity and bandwidth limitations directly constrain model scale and inference throughput. PCB-level solutions must deliver sustained high bandwidth while minimizing parasitic inductance, crosstalk, and impedance discontinuities that degrade signal quality at multi-GHz speeds.
High-bandwidth memory (HBM) stacks and GDDR6 interfaces require precise PCB stack-up planning. Designers typically employ low-loss dielectric materials (such as modified FR4 or specialized laminates with low Dk/Df) and controlled impedance routing to preserve eye openings. Copper thickness and via-in-pad technology help manage current density and reduce resistance in power delivery networks supporting these dense memory modules.
Processing-in-Memory (PIM) and Compute-in-Memory (CiM) Integration
SK hynix's AiM (Accelerator-in-Memory) and Samsung's HBM-PIM/LPDDR-PIM architectures embed simple processing elements—such as ALUs operating at ~1 GHz—directly into memory tiles. This enables internal bandwidths exceeding 512 GB/s per tile for GEMV operations, drastically reducing off-chip data movement. Samsung's -PNM (Processing Near Memory) extends this concept to disaggregated modules, delivering conceptual bandwidths up to 1.1 TB/s on 512 GB cards.
From a PCB perspective, integrating PIM modules demands careful attention to power distribution, thermal vias, and high-speed differential pairs. The proximity of compute logic to dense memory arrays increases localized heat generation, requiring enhanced thermal relief patterns, thicker copper planes, and sometimes metal-core or embedded heat-spreader PCBs. Signal integrity simulations must account for the interaction between memory command interfaces and embedded processing logic to prevent timing violations.
Memory Type Selection and PCB Implications for In-Memory Computing Chips
Choosing the right memory technology—SRAM, DRAM, RRAM, or MRAM—depends on application requirements for speed, endurance, power, and density:
- SRAM and DRAM excel in high-compute scenarios due to fast read/write speeds but consume more power and area.
- RRAM and MRAM offer non-volatility and lower standby power, suiting edge AI devices, yet present endurance limits (typically 10^6–10^8 cycles) and process integration challenges.
PCB designers must accommodate the electrical characteristics of each technology. RRAM-based arrays, for example, benefit from analog-friendly routing with minimal noise coupling, while high-density SRAM/DRAM stacks require robust power integrity and decoupling strategies. Analog CiM implementations leverage Ohm's and Kirchhoff's laws for multiply-accumulate operations and often pair with sensor interfaces, necessitating mixed-signal PCB partitioning to isolate sensitive analog traces from digital switching noise.
High-compute designs frequently parallelize multiple cores, mirroring GPU architectures. This increases pin counts and requires advanced packaging techniques such as 2.5D interposers or fan-out wafer-level packaging, which in turn influence motherboard or module PCB layouts with finer line/spacing rules and HDI (high-density interconnect) features.
Hybrid Memory Disaggregation: CXL and RDMA on the PCB Level
Memory disaggregation pools resources across compute and memory nodes to improve utilization and scalability. Pure RDMA solutions suffer microsecond-scale latencies, while pure CXL offers near-DRAM access (90–150 ns) but is constrained to rack-level distances.
Hybrid RDMA+CXL architectures, such as those explored in research prototypes like Rcmp, combine
CXL coherence with inter-rack RDMA scalability. PCB implementations of CXL interfaces must support PCIe 5.0/6.0 signaling with retimers, precise clock distribution, and low-skew routing. Hot-page migration and cache-line granularity access require sophisticated memory controllers whose high-speed links demand careful via stub minimization and back-drilling on the PCB.
Thermal management becomes critical in disaggregated systems, as memory nodes may operate continuously under heavy AI workloads. Designers incorporate thermal interface materials, optimized copper pours, and sometimes active cooling solutions directly into the PCB assembly.
LLM-Driven Storage Requirements and PCB Reliability Considerations
LLMs accelerate the shift toward multimodal datasets (text, images, audio, video), pushing storage from terabyte to petabyte scales. Challenges include slow data ingestion, frequent checkpointing, and the need for near-data preprocessing to reduce movement overhead.
PCBs supporting these storage subsystems must ensure long-term reliability under high IOPS and sustained bandwidth. Key practices include:
- Selecting high-Tg laminates for thermal stability during reflow and operation.
- Implementing redundant power planes and robust grounding to mitigate EMI in dense AI server environments.
- Designing for manufacturability with appropriate drill-to-copper clearances and aspect ratios suited to high-layer-count boards (often 20+ layers for AI accelerators).
Failure mechanisms such as electromigration in power traces or delamination under thermal cycling must be addressed through proper material qualification and design rules.
Future Trends in PCB Design for Memory-Centric AI Systems
The next wave of memory-centric computing will accelerate several PCB technology shifts. Advanced 3D heterogeneous integration and chiplet architectures will demand finer-pitch interconnects, embedded bridges, and glass or organic interposers on the PCB side. Emerging CXL 3.0 and PCIe 7.0 standards will push signaling speeds beyond 64 GT/s, requiring ultra-low-loss materials, advanced via technologies such as stacked microvias, and real-time signal integrity monitoring embedded in the board.
Optical interconnects and co-packaged optics are gaining traction for disaggregated memory fabrics, introducing new challenges in hybrid electrical-optical PCB layouts and thermal management of photonic engines. Sustainable manufacturing practices—such as halogen-free laminates, recycled copper, and energy-efficient fabrication processes—will become essential as data centers scale AI infrastructure while meeting environmental regulations.
AI-assisted PCB design tools leveraging machine learning will optimize stack-ups, routing, and thermal profiles in real time, shortening development cycles for complex PIM and CiM modules. Edge AI deployments will drive demand for flexible and rigid-flex PCBs that maintain signal integrity under vibration and temperature extremes while supporting compact, high-density memory-compute integration.
Conclusion
Memory-centric architectures such as PIM, CiM, and hybrid CXL/RDMA disaggregation represent a fundamental shift in how AI systems overcome the memory wall. Realizing their performance and efficiency gains ultimately depends on PCB engineering excellence—precise stack-up design, signal and power integrity, thermal dissipation, and manufacturability. By embedding PCB considerations early in the system architecture phase, engineers can transform innovative memory technologies into reliable, high-volume production solutions.
As AI workloads continue to grow in scale and complexity, collaboration between memory innovators, system designers, and PCB manufacturers will be critical. Aivon remains committed to delivering advanced PCB fabrication and engineering support that enables the next generation of memory-centric AI hardware across data centers, edge devices, and specialized accelerators.
