# SCALE-Sim: Systolic CNN Accelerator Simulator

arXiv: https://arxiv.org/abs/1811.02883 · HTML: https://mlsanity.com/paper/1811.02883/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 26 of 26)

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- [S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration](https://mlsanity.com/paper/2107.07983/) (2021)
- [Control Variate Approximation for DNN Accelerators](https://mlsanity.com/paper/2102.09642/) (2021)
- [TransAxx: Efficient Transformers with Approximate Computing](https://mlsanity.com/paper/2402.07545/) (2024)
- [Scale-out Systolic Arrays](https://mlsanity.com/paper/2203.11540/) (2022)
- [SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators](https://mlsanity.com/paper/2301.10388/) (2023)
- [TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT](https://mlsanity.com/paper/2104.02233/) (2021)
- [QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators](https://mlsanity.com/paper/2205.08648/) (2022)
- [Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units](https://mlsanity.com/paper/2304.09258/) (2023)
- [QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality](https://mlsanity.com/paper/2205.13045/) (2022)
- [Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA](https://mlsanity.com/paper/2208.04854/) (2022)
- [Integration of a systolic array based hardware accelerator into a DNN operator auto-tuning framework](https://mlsanity.com/paper/2212.03034/) (2022)
- [S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks](https://mlsanity.com/paper/2106.07894/) (2021)
- [QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration](https://mlsanity.com/paper/2206.15463/) (2022)
- [Self-Adaptive Reconfigurable Arrays (SARA): Using ML to Assist Scaling GEMM Acceleration](https://mlsanity.com/paper/2101.04799/) (2021)
- [CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs](https://mlsanity.com/paper/2107.09408/) (2021)
- [Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge](https://mlsanity.com/paper/2108.11441/) (2021)
- [PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator](https://mlsanity.com/paper/2211.05276/) (2022)
- [Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators](https://mlsanity.com/paper/2301.09813/) (2023)
- [SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks](https://mlsanity.com/paper/2505.12292/) (2025)
- [A Construction Kit for Efficient Low Power Neural Network Accelerator Designs](https://mlsanity.com/paper/2106.12810/) (2021)
- [Compiler-Driven Simulation of Reconfigurable Hardware Accelerators](https://mlsanity.com/paper/2202.00739/) (2022)
- [DRAGON (Differentiable Graph Execution) : A suite of Hardware Simulation and Optimization tools for Modern AI/Non-AI Workloads](https://mlsanity.com/paper/2204.06676/) (2022)

## Abstract (arXiv)

Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications. However, the research community lacks tools to insights on both the design trade-offs and efficient mapping strategies for systolic-array based accelerators. We introduce Systolic CNN Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based cycle accurate DNN accelerator simulator. SCALE-Sim exposes various micro-architectural features as well as system integration parameters to the designer to enable comprehensive design space exploration. This is the first systolic-array simulator tuned for running DNNs to the best of our knowledge. Using SCALE-Sim, we conduct a suite of case studies and demonstrate the effect of bandwidth, data flow and aspect ratio on the overall runtime and energy of Deep Learning kernels across vision, speech, text, and games. We believe that these insights will be highly beneficial to architects and ML practitioners.