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Benefits

Using the HBM block in VisualSim provides:

  • Bandwidth Scaling: Evaluate memory throughput under multi-stack configurations.
  • Latency Analysis: Quantify improvements vs. DDR, LPDDR, and GDDR.
  • Power Optimization: Study trade-offs in energy per bit transferred.
  • Failure Analysis: Model ECC, fault tolerance, and pseudo-channel resilience.
  • Cross-Domain Relevance: Applicable across AI, HPC, graphics, automotive, networking, and aerospace.
  • Design Trade-offs: Compare HBM generations (HBM2, HBM2E, HBM3, HBM3E).

The **HBM (High Bandwidth Memory) block** in VisualSim models **3D-stacked DRAM** that delivers ultra-high memory bandwidth by placing DRAM dies vertically on a base logic die and connecting them using **TSVs (Through-Silicon Vias)**. By integrating multiple DRAM stacks close to the processing units, HBM drastically reduces latency, improves energy efficiency, and eliminates memory bottlenecks common in AI and HPC systems.

HBM technology was standardized by JEDEC in 2013 and commercialized in 2015 with AMD’s Fiji GPU. Since then, major semiconductor and system vendors have adopted HBM across diverse industries:

  • NVIDIA, AMD, and Intel: use HBM in GPUs and AI accelerators.
  • Samsung, Micron, SK Hynix: are the primary HBM DRAM suppliers.
  • Tesla, Google, and Microsoft: deploy HBM-powered accelerators in datacenters.
  • Defense and Aerospace organizations: use HBM for space and mission-critical computing.

The HBM block in VisualSim allows architects to evaluate timing, bandwidth utilization, pseudo-channel behavior, and power-performance trade-offs, making it vital for next-generation SoC and system design.

Overview

The HBM block in VisualSim provides the following features:

  • Multiple Memory Controllers: Coordinate data transactions across stacked DRAM dies.
  • Independent Memory Channels: Allow simultaneous handling of multiple requests.
  • Pseudo Channels: Optimize access efficiency in HBM2 and later standards.
  • Address Mapping System: Selects memory channels based on request addresses.
  • Buffered Request Handling: Queues input requests when memory is occupied.
  • Scalability: Supports multi-stack configurations for AI accelerators and HPC systems.
  • Energy Efficiency: Models low-power access compared to DDR/GDDR alternatives.

Supported Standards

The HBM block aligns with JEDEC HBM standards, which evolved across generations:

  • HBM (HBM1, JESD235, 2013): 4 GB per stack, up to 128 GB/s bandwidth.
  • HBM2 (JESD235A/B, 2016): Up to 8 GB per stack, 256 GB/s bandwidth, pseudo-channels.
  • HBM2E (2019): 16–24 GB per stack, 410 GB/s bandwidth.
  • HBM3 (JESD238, 2022): 24–64 GB per stack, 819 GB/s bandwidth, ECC support.
  • HBM3E (2024+): Extends bandwidth beyond 1.2 TB/s per stack, optimized for AI/HPC.

Key Parameters

Configurable parameters include:

  • Timing_Cycles: Defines read/write latencies.
  • Memory_Type: HBM1, HBM2, HBM2E, HBM3, HBM3E.
  • DRAM_Speed_MHz: Data rate per channel.
  • Stack_Count: Number of HBM stacks.
  • Channel_Count: Independent channels per stack.
  • Pseudo_Channel_Enable: Optimizes HBM2/3 parallelism.
  • ECC_Enable: Error correction for reliability.
  • Power_Profile: Models energy efficiency per access.

Parameter Snapshots

  • Parameter 1

Simulation Results

  • Latency vs. Bandwidth Reports: Compare performance across workloads.
  • Stack Utilization Reports: Bandwidth scaling with multiple HBM stacks.
  • Energy Efficiency Studies: Power per bit vs. DDR/GDDR alternatives.
  • ECC Reliability Reports: Error detection and correction metrics.
  • Latency
  • Stats

Applications

HBM is used in industries that demand extreme memory bandwidth and low latency:

  • AI & Machine Learning:
    • Training LLMs and deep neural networks.
    • Inference workloads for real-time analytics.
  • High-Performance Computing (HPC):
    • Supercomputers for scientific simulations and weather modeling.
    • Exascale-class parallel computing.
  • Graphics & Gaming:
    • GPUs for professional rendering, CAD, VR/AR, and high-end gaming.
  • Automotive:
    • ADAS and autonomous driving platforms requiring sensor fusion.
    • Real-time image processing and decision-making.
  • Networking & 5G Infrastructure:
    • Packet processing, encryption, and real-time data analytics.
  • Aerospace & Defense:
    • Space-qualified HBM for satellites and avionics.
    • Secure, fault-tolerant mission systems.
  • Datacenters & Cloud AI:
    • Google TPU, NVIDIA H100, AMD MI300 all leverage HBM for AI workloads.
    • Reduces energy per bit transferred compared to DDR/GDDR.

Integrations

  • Integrates with processors, GPUs, NPUs, and accelerators for heterogeneous workloads.
  • Works with interconnect models (CXL, UCIe, CoreLink, Arteris NoC) for full SoC studies.
  • Can be combined with DDR, LPDDR, and cache models for hybrid memory hierarchies.
  • Supports chiplet-based exploration, where HBM stacks are integrated via 2.5D/3D packaging.

View Demo

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