# SCALE-Sim v3: A modular cycle-accurate systolic accelerator simulator for end-to-end system analysis

arXiv: https://arxiv.org/abs/2504.15377 · HTML: https://mlsanity.com/paper/2504.15377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2025.

## Built on by (most cited first, 9 of 9)

- [FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors](https://mlsanity.com/paper/2605.08594/) (2026)
- [Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators](https://mlsanity.com/paper/2511.17971/) (2025)
- [Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling](https://mlsanity.com/paper/2512.22066/) (2025)
- [REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligence](https://mlsanity.com/paper/2601.20784/) (2026)
- [ChatNeuroSim: An LLM Agent Framework for Automated Compute-in-Memory Accelerator Deployment and Optimization](https://mlsanity.com/paper/2603.08745/) (2026)
- [SISA: A Scale-In Systolic Array for GEMM Acceleration](https://mlsanity.com/paper/2603.29913/) (2026)
- [AgentDSE: Reasoning-Augmented Architectural Design Space Exploration](https://mlsanity.com/paper/2606.21836/) (2026)
- [Heterogeneity-Aware Microscaling for Efficient Low-Bit LLM Inference](https://mlsanity.com/paper/2608.03867/) (2026)
- [PerfReasoning: How Well Do LLMs Reason on Hardware Performance?](https://mlsanity.com/paper/2609.04476/) (2026)

## Abstract (arXiv)

The rapid advancements in AI, scientific computing, and high-performance computing (HPC) have driven the need for versatile and efficient hardware accelerators. Existing tools like SCALE-Sim v2 provide valuable cycle-accurate simulations for systolic-array-based architectures but fall short in supporting key modern features such as sparsity, multi-core scalability, and comprehensive memory analysis. To address these limitations, we present SCALE-Sim v3, a modular, cycle-accurate simulator that extends the capabilities of its predecessor. SCALE-Sim v3 introduces five significant enhancements: multi-core simulation with spatio-temporal partitioning and hierarchical memory structures, support for sparse matrix multiplications (SpMM) with layer-wise and row-wise sparsity, integration with Ramulator for detailed DRAM analysis, precise data layout modeling to minimize memory stalls, and energy and power estimation via Accelergy. These improvements enable deeper end-to-end system analysis for modern AI accelerators, accommodating a wide variety of systems and workloads and providing detailed full-system insights into latency, bandwidth, and power efficiency. A 128x128 array is 6.53x faster than a 32x32 array for ViT-base, using only latency as a metric. However, SCALE-Sim v3 finds that 32x32 is 2.86x more energy-efficient due to better utilization and lower leakage energy. For EdP, 64x64 outperforms both 128x128 and 32x32 for ViT-base. SCALE-Sim v2 shows a 21% reduction in compute cycles for six ResNet18 layers using weight-stationary (WS) dataflow compared to output-stationary (OS). However, when factoring in DRAM stalls, OS dataflow exhibits 30.1% lower execution cycles compared to WS, highlighting the critical role of detailed DRAM analysis.