EDA Storage Cluster – empowerx.com.hk

Semiconductor EDA / IC Design Computing & Storage Cluster Solution

As chip design complexity continues to increase, EDA (Electronic Design Automation) simulation platforms have become a core infrastructure component for semiconductor enterprises.

This article provides a structured overview of:

The main functional modules of an EDA simulation platform
The computational characteristics and core algorithm features of each module
Hardware configuration recommendations based on simulation task types
A reference architecture for a high-performance EDA / IC design computing and storage cluster

The objective is to support efficient, scalable, and stable chip development workflows in advanced semiconductor environments.

1. Major Modules of an EDA Simulation Platform

An EDA platform spans the entire IC design flow, from system-level modeling to physical verification. Key modules include:

System-Level Modeling & Simulation

High-level abstraction and algorithm validation using tools such as SystemC and MATLAB/Simulink.
Compute profile: Moderate workload, primarily dependent on single-thread CPU performance.

RTL Design & Functional Simulation

RTL development using Verilog/VHDL, followed by functional verification with simulators such as VCS and Xcelium.
Compute profile: High-frequency CPUs, large memory capacity, multi-thread support.

Formal Verification

Equivalence checking and property verification using formal tools.
Compute profile: Memory-intensive, limited multi-core scalability depending on tool architecture.

Logic Synthesis

Conversion from RTL to gate-level netlist.
Compute profile: Strong dependence on single-core performance, moderate parallelization.

Static Timing Analysis (STA)

Timing closure validation for advanced process nodes.
Compute profile: High memory usage, moderate multi-thread scaling.

Power Analysis

Dynamic and static power estimation from switching activity.
Compute profile: Large waveform data processing, high storage I/O demand.

Place & Route (P&R)

Backend physical implementation.
Compute profile: Compute-intensive, benefits from multi-core parallelism and large memory capacity.

Electromagnetic / Signal Integrity Analysis

IR drop, crosstalk, and power network analysis.
Compute profile: High memory consumption; certain workloads can leverage GPU acceleration.

DRC / LVS Physical Verification

Design Rule Checking and Layout Versus Schematic validation.
Compute profile: Distributed parallel execution; high I/O throughput requirement.

Gate-Level Simulation (GLS)

Final-stage functional and timing verification.
Compute profile: High I/O bandwidth and memory demand; supports distributed simulation.

II. Computational Characteristics by Module

Different EDA workloads exhibit distinct compute patterns:

Module	CPU Scaling	Memory Demand	Storage I/O	GPU Acceleration
System Modeling	Single-thread dominant	Low	Low	Not required
RTL Simulation	High-frequency multi-core	Medium–High	Medium	Limited
Formal Verification	Limited scaling	Very High	Low	Not typical
Logic Synthesis	Single-core dominant	Medium	Low	Not typical
STA	Moderate parallelism	High	Low	Not typical
Power Analysis	Moderate	Medium	High	Limited
Place & Route	Strong parallel scaling	High	Medium	Optional
EM / IR Analysis	Moderate	Very High	Medium	Supported
DRC / LVS	Distributed scaling	Medium	High	Not typical
GLS	Distributed scaling	High	High	Not typical

This differentiation is critical when designing an optimized compute infrastructure.

III. Hardware Configuration Recommendations by Workload

Functional Simulation & Logic Synthesis

CPU: High-frequency Intel Xeon or AMD EPYC
Core count: 16–128 cores (for multi-job parallel runs)
Memory: 128GB–2TB (depending on design scale)
Storage: High-IOPS NVMe SSD
Network: 10GbE or InfiniBand

Timing / Power / Gate-Level Simulation

CPU: High-frequency multi-core processors (4.0GHz+)
Memory: ≥128GB
Storage: NVMe + RAID configuration
Recommendation: Local high-speed cache disk (e.g., RAM disk) for waveform processing

Backend Place & Route & EM Simulation

CPU: Large core count (64+ cores recommended)
Memory: 512GB–2TB (large designs require higher capacity)
Optional GPU: NVIDIA professional GPU series for acceleration
Network: 25GbE or higher for cluster communication

DRC / LVS (Distributed Physical Verification)

Per node: 16 cores, 256GB memory
Storage: High-concurrency enterprise NVMe RAID or distributed storage (Lustre/NFS)
Scheduler: LSF, SLURM, or dedicated EDA workload manager

IV. EDA Simulation Platform Server / Cluster Architecture

To meet high-performance EDA workloads, the following infrastructure tiers are recommended:

Development Workstation

16–128 core CPU
128GB RAM
2TB NVMe SSD
Professional GPU for GUI-based visualization

Dedicated Simulation Server Nodes

32+ cores
512GB RAM
4TB+ SSD
High-speed networking

Parallel Simulation Cluster

4–32 compute nodes
Shared distributed storage (NFS or Lustre)
Centralized job scheduling (LSF or SLURM)
High-speed switching (25–100GbE)

V. Reference EDA / IC Design Computing & Storage Cluster

To address the computational demands of semiconductor design teams, a modular cluster architecture is proposed, consisting of:

NO	Item	Description	Qty
1	Front-end design serve	Dual high-core-count enterprise CPUs 1TB DDR5 ECC memory 2 NVMe SSD storage Dual 100GbE network connectivity 2U rackmount configuration	1
2	Backend design server	High-frequency workstation-class CPUs 128GB–512GB DDR5 ECC memory 2 NVMe SSD Professional GPU (48GB class) Dual 25GbE connectivity 4U rack/tower convertible chassis	5
3	All-Flash Storage Server	Dual enterprise CPUs 100TB-class NVMe RAID array 100GbE connectivity Designed for high-concurrency EDA I/O workloads	1
4	High-Capacity Storage Server	Hybrid NVMe cache + large-capacity HDD array (200TB+) 100GbE connectivity Suitable for long-term project data retention	1
5	Network & Infrastructure	100GbE high-speed core switch Gigabit management switch 42U rack cabinet with PDU Centralized KVM management	1

Conclusion: Computing Power as the Foundation of Chip Design Innovation

Driven by advanced process nodes, high-density packaging, and emerging 3D IC architectures, EDA platforms face rapidly increasing computational demands.

A properly architected computing and storage cluster — optimized per workload type — significantly improves simulation throughput, resource utilization, and overall project turnaround time.

By aligning infrastructure design with EDA workload characteristics, semiconductor organizations can build scalable, high-efficiency platforms that support continuous innovation in complex IC development environments.