---
title: Chiplet-Based Memory Modules
url: https://www.emergentmind.com/topics/chiplet-based-memory-modules
type: topic
---

# Chiplet-Based Memory Modules

Chiplet-based memory modules are modular, physically disaggregated memory sub-systems assembled from independently fabricated chiplets—specialized dies such as DRAM, SRAM, non-volatile memory, or memory controllers—interconnected within a single package or on a shared interposer. By leveraging advanced packaging (2.5D or 3D integration), chiplet-based memory modules address the historic bottlenecks of monolithic dies: limited reticle size, technology lock-in, diminishing yield with die area scaling, and sub-optimal trade-offs between bandwidth, latency, and memory capacity. Modern accelerators for high-performance computing (HPC) and machine learning (ML) exploit these architectures to achieve algorithm-tailored bandwidth, maximize compute-to-memory proximity, and enable fine-grained resource scaling for diverse workloads [2501.07330][2511.15397][2510.06513][2312.11750][2511.12286][2311.15443][2108.08903][2307.02666][2406.15068].

## 1. Physical Organization and Packaging Technologies

Chiplet-based memory modules use a diverse set of integration strategies, typically falling into 2.5D (multiple dies on a passive silicon or organic interposer) or 3D (vertical stacking with through-silicon vias, TSVs) approaches. An illustrative example is Occamy, where two 73 mm² compute chiplets (fabricated in GlobalFoundries 12 nm FinFET) and two HBM2E memory stacks are mounted face-down on a 65 nm passive silicon interposer (“Hedwig”). This interposer provides up to sixteen power/ground domains, routes high-speed HBM2E data channels (eight per stack, <4.9 mm length, 2.5 µm trace/4.1 µm pitch), and supports point-to-point die-to-die (D2D) links with energy-efficient signaling (~1.6 pJ/bit) [2501.07330][2406.15068].

Other approaches, such as UCIe-based memory modules, enable direct logic-to-memory (SoC-to-DRAM or SoC-to-LPDDR6) connectivity using advanced PHYs at fine bump pitches (down to 25 µm), significantly improving areal bandwidth density relative to conventional wide, parallel DRAM buses [2510.06513]. Heterogeneous integration (HI), where DRAM, SRAM, non-volatile ReRAM, and controller logic are fabricated in independent process nodes, further optimizes per-component performance and yield [2312.11750][2511.12286][2511.15397].

## 2. Memory Architecture, Interconnect Topology, and Dataflow

Chiplet-based memory architectures are characterized by hierarchical, multi-level interconnects and explicitly managed data movement. In Occamy, for example, each compute chiplet attaches to an HBM2E stack through eight wide PHY+controller macros, interfacing to the chiplet core clusters via a crossbar hierarchy: cluster-level (nine cores sharing 128 KiB scratchpad RAM), group-level (four clusters per group with independent 512-bit crossbars, 64 GB/s), and chiplet-level (aggregating to 381.5 GB/s raw DRAM bandwidth per chiplet; two chiplets → 763 GB/s aggregate) [2501.07330]. The D2D links allow for distributed execution, with measured inter-chiplet access latencies in the 27–61 cycle range.

A different design employed in Hemlet uses a heterogeneous collection of analog-CIM (RRAM), digital-CIM (SRAM), and intermediate data process (IDP) chiplets. These are interconnected via a mesh network-on-package (NoP), with up to eight unidirectional links per chiplet (32 GB/s per link), supporting total NoP bandwidth up to 256 GB/s [2511.15397]. In highly parallel systems such as SIAM or DCRA, the interconnect takes the form of a folded torus or 2D mesh at both intra-die (NoC) and inter-die (NoP) levels, facilitating reconfigurability and robust scaling for irregular or sparse workloads [2108.08903][2311.15443].

The dataflow in these systems is typically managed via scratchpad- or DMA-driven explicit data movement, eschewing general-purpose caching in favor of deterministic latency, bandwidth partitioning, and contention management. The lack of hardware cache coherence and the use of software-managed memory hierarchies is prevalent in high-throughput, deterministic accelerator designs [2501.07330][2312.11750][2406.15068].

## 3. Memory Technologies and Performance Metrics

DRAM-based chiplet modules usually employ wide I/O standards (HBM2E, HBM4, LPDDR6) delivered in stacked configurations, each channel delivering on the order of ~50 GB/s with eight or more channels per stack. For instance, the Occamy architecture has each HBM2E stack delivering 8 × 47.68 GB/s ≈ 381.5 GB/s per chiplet [2501.07330]. Performance is characterized by sustained bandwidth, latency (10–150 ns depending on memory level and data movement software), utilization (e.g., 83% FPU utilization for stencil codes in Occamy), and compute-to-bandwidth ratios (B/F).

Non-volatile memory technologies such as RRAM are used in in-memory-compute (IMC) modules, supporting multi-bit cell encoding (e.g., two bits per RRAM cell), analog or digital vector-matrix multiplication, and are often organized as crossbar arrays partitioned into tiles, groups, and processing engines [2511.15397][2108.08903].

Energy and area efficiency is central: in SIAM, a chiplet-based IMC system achieves up to 130× energy-efficiency relative to a V100 GPU, with per-flit interconnect energy ~0.54 pJ/bit (NoP) and crossbar compute energy modeled per operation [2108.08903]. UCIe-based DRAM modules demonstrate up to 10× higher bandwidth density and up to 3× lower energy-per-bit vs. HBM4 or LPDDR6 [2510.06513].

## 4. Design Trade-offs, Scalability, and Technology Flexibility

Chiplet-based memory modules expose multiple axes of design-time and run-time tradeoffs. At the silicon level, smaller chiplets yield higher manufacturing yield, enable per-function process optimization (logic at advanced nodes, DRAM at cost- or density-optimized nodes), and decouple reticle-size constraints [2501.07330][2311.15443]. However, increased chiplet count can increase package and interposer overhead, with crossbar and interconnect area potentially occupying up to 25% of total die area [2501.07330][2307.02666].

High-radix crossbars or bisection-bandwidth NoC designs improve aggregate bandwidth at an area and power penalty. Fine-grained power domains (core, PHY, DRAM, I/O), multi-level voltage/frequency islands, and hierarchical interconnect segmentation reduce system-level power consumption and thermal hotspots [2501.07330].

Systems such as Sangam demonstrate the separation of memory and compute chiplets—DRAM process for dense memory arrays, logic node for bank-attached PIM engines—enabling full DRAM capacity utilization, higher logic density and performance, and support for CXL-attached pooled memory [2511.12286]. UCIe-based modules offer technology-agnostic memory extension and cost-efficient reuse of commodity DRAM devices, further decoupling technology lock-in [2510.06513].

Scalability is maintained by linear (or near-linear) replication: bandwidth, memory capacity, and compute resources scale with chiplet count, modulo global bottlenecks (NoP, DMA engine saturation, interposer bandwidth). Configurability at compile-time (e.g., SRAM cache vs. scratchpad partition, logical grid shape, software task mapping) and package-time (chiplet counts and placement) further enable hardware-software co-optimization across applications [2311.15443][2307.02666].

## 5. System-Level Integration, Application Mapping, and Use Cases

Chiplet-based memory modules are now foundational in heterogeneous accelerators for ML (transformers, LLMs, ViTs), HPC (stencil, SpMM), and data-analytic workloads (graph, sparse linear algebra). Examples include:

- **ML Accelerators**: Hemlet achieves 1.44–4.07× system speedup vs. monolithic CIM, supporting on-chip ViT model storage and high-throughput inference via analog/digital-heterogeneous arrays [2511.15397].
- **HPC**: Occamy demonstrates up to 89% dense DGEMM FPU utilization and 42% on sparse-dense LA kernels, with up to 11× improvement in normalized compute density over previous designs [2501.07330][2406.15068].
- **LLM Serving**: Sangam modules, CXL-attached, act as GPU co-processors or replacements and enable order-of-magnitude throughput and energy gains for memory-bound LLM decoding kernels [2511.12286]. Chiplet Cloud aggregates thousands of chiplets to deliver petabyte-scale SRAM-backed memory for LLMs at up to 97× lower cost/Token than GPUs [2307.02666].

Tables elucidate decisive architectural parameters and performance characteristics:

| System          | Memory Chiplets        | Integration     | Peak BW / Module | Use Cases                |
|-----------------|-----------------------|-----------------|------------------|--------------------------|
| Occamy          | 2 × HBM2E, 8 ch/stack | 2.5D, Si interposer | 763 GB/s         | Dense/sparse ML, HPC     |
| Hemlet          | RRAM/SRAM CIM         | 2.5D, NoP mesh  | 256 GB/s (NoP)   | ViT Inference            |
| Sangam          | DRAM+Logic (decoupled)| 2.5D, CXL attach| Up to 400 GB/s/chip| LLM memory-bound kernels |
| Chiplet Cloud   | SRAM w/ Compression   | Intra-chip + 2D torus| 2.75 TB/s/chip  | Large LLM serving        |

Memory performance, capacity, and interconnect configure according to the algorithmic dataflow’s demands: high-parallelism streaming, burst access, or irregular pointer-chasing all have optimized mapping strategies [2312.11750][2311.15443][2108.08903].

## 6. Analytical Models, Lessons Learned, and Design Guidelines

Quantitative performance modeling is integral for architecture tuning:

- **Bandwidth Formula** (per chiplet): $$BW_{\text{peak}} = N_{c} \cdot b_{\text{per chan}} \cdot f_{\ell}$$ where $N_{c}$ is channel count, $b_{\text{per chan}}$ is width, and $f_{\ell}$ is channel I/O frequency [2501.07330].
- **Effective Bandwidth per Tile (DCRA)**: $$E_{\text{BW}} = B_{\text{SRAM}} \cdot H + B_{\text{HBM}} \cdot (1-H)$$ where $H$ is cache/scratchpad hit rate [2311.15443].
- **Latency Models**: Aggregate of on-chip cycles, crossbar/interposer hops, and round-trip wire/PHY costs [2501.07330][2108.08903].
- **Energy per Bit/Access**: Ranges from 0.18 pJ/bit (SRAM read) to 3.7 pJ/bit (HBM2E transfer), with interposer and NoP overhead tuned by signaling regime and distance [2311.15443][2511.15397][2108.08903].

Key principles include:
- Hierarchical crossbar symmetry enables simpler software mapping and uniform QoS [2501.07330].
- Explicit DMA and scratchpad regimes are critical for high sustained utilization, especially with double buffering [2501.07330][2406.15068].
- Software-driven configuration (e.g., mesh/torus toggling, cache allocation, task routing) underpins sustained bandwidth utilization and latency hiding across irregular workloads [2311.15443].

Economic analysis (yield, manufacturing, assembly cost) favors smaller, more numerous chiplets for high-yield, flexible process targeting; however, this requires careful optimization of crossbar/interposer area, periphery wiring, and interconnect signaling to avoid diminishing returns with scaling [2307.02666][2510.06513].

## 7. Outlook, Technology Gaps, and Research Directions

Chiplet-based memory modules represent an inflection point in memory and accelerator architecture. Open research topics include:
- Energy-performance trade-off tuning under extreme device heterogeneity and aggressive NRE/power cost constraints [2311.15443][2501.07330].
- End-to-end cohesive support for memory-compute disaggregation in next-generation domain-specific accelerators (e.g., integrating processing-in-memory for low-operational-intensity workloads) [2511.12286].
- Thermal and mechanical management in large-scale, high-bandwidth chiplet assemblies, particularly as package power density increases with finer bump pitches and multi-stack DRAM [2501.07330][2510.06513].
- Robust, technology-agnostic interfaces (UCIe, CXL, proprietary D2D) supporting seamless interoperation across memory, logic, and host subsystems [2510.06513][2511.12286].
- High-level synthesis, co-design tools, and runtime-aware scheduling that exploit the explicit data movement and topology configurability of chiplet memory modules [2307.02666][2311.15443].

Chiplet-based memory modules will remain central to memory- and bandwidth-bound application domains as advanced packaging, process decoupling, and architectural co-optimization continue to redefine the limits of system performance, energy efficiency, and cost scaling.

Source: https://www.emergentmind.com/topics/chiplet-based-memory-modules