# DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era

arXiv: https://arxiv.org/abs/2102.10195 · HTML: https://mlsanity.com/paper/2102.10195/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2021.

## Built on by (most cited first, 30 of 30)

- [OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization](https://mlsanity.com/paper/2304.07493/) (2023)
- [ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization](https://mlsanity.com/paper/2208.14286/) (2022)
- [MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization](https://mlsanity.com/paper/2411.05282/) (2024)
- [Accelerating Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15246/) (2024)
- [Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference](https://mlsanity.com/paper/2403.05465/) (2024)
- [Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving](https://mlsanity.com/paper/2507.10178/) (2025)
- [Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Acceleration](https://mlsanity.com/paper/2511.12930/) (2025)
- [Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks](https://mlsanity.com/paper/2206.09951/) (2022)
- [Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator](https://mlsanity.com/paper/2504.14365/) (2025)
- [STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support](https://mlsanity.com/paper/2506.09070/) (2025)
- [P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats](https://mlsanity.com/paper/2511.06838/) (2025)
- [QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration](https://mlsanity.com/paper/2206.15463/) (2022)
- [OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads](https://mlsanity.com/paper/2508.08822/) (2025)
- [Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications](https://mlsanity.com/paper/2206.06780/) (2022)
- [Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication](https://mlsanity.com/paper/2311.10207/) (2023)
- [Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors](https://mlsanity.com/paper/2409.17341/) (2024)
- [VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator](https://mlsanity.com/paper/2507.00797/) (2025)
- [DISCA: A Digital In-memory Stochastic Computing Architecture Using A Compressed Bent-Pyramid Format](https://mlsanity.com/paper/2511.17265/) (2025)
- [DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction](https://mlsanity.com/paper/2607.15846/) (2026)
- [Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator](https://mlsanity.com/paper/2204.09797/) (2022)
- [COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks](https://mlsanity.com/paper/2205.07311/) (2022)
- [Energy Efficient Exact and Approximate Systolic Array Architecture for Matrix Multiplication](https://mlsanity.com/paper/2509.00778/) (2025)
- [GAVINA: flexible aggressive undervolting for bit-serial mixed-precision DNN acceleration](https://mlsanity.com/paper/2511.23203/) (2025)
- [REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligence](https://mlsanity.com/paper/2601.20784/) (2026)
- [ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration](https://mlsanity.com/paper/2605.22015/) (2026)
- [CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation](https://mlsanity.com/paper/2606.02781/) (2026)
- [BenDi: An Energy-Efficient Quasi-Stochastic Systolic Architecture for Edge Bioelectronics](https://mlsanity.com/paper/2606.12235/) (2026)
- [HiKV: Hierarchical Importance-Aware KV Cache with Hardware Acceleration for LLM Decoding](https://mlsanity.com/paper/2607.22389/) (2026)
- [Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference](https://mlsanity.com/paper/2608.04428/) (2026)
- [NOVA: Technology-Architecture Co-Design of Near-Memory Processing for Attention-SSM-MoE Hybrid LLM Inference](https://mlsanity.com/paper/2608.22613/) (2026)

## Abstract (arXiv)

The estimation of classical CMOS "constant-field" or "Dennard" scaling methods that define scaling factors for various dimensional and electrical parameters have become less accurate in the deep-submicron regime, which drives the need for better estimation approaches especially in the educational and research domains. We present DeepScaleTool, a tool for the accurate estimation of deep-submicron technology scaling by modeling and curve fitting published data by a leading commercial fabrication company for silicon fabrication technology generations from 130~nm to 7~nm for the key parameters of area, delay, and energy. Compared to 10~nm--7~nm scaling data published by a leading foundry, the DeepScaleTool achieves an error of 1.7% in area, 2.5% in delay, and 5% in power. This compares favorably with another leading academic estimation method that achieves an error of 24% in area, 9.1% in delay, and 24.9% in power.