mlsanityData to 7 Oct 2026

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

arXiv 2102.10195 ↗First posted 19 Feb 2021eess.SY cs.AR cs.PFcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2021.

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Citing papers per year: 2022: 6; 2023: 1; 2024: 4; 2025: 10; 2026: 6.

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  1. 2022ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantizationcited by 42
  2. 2024MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantizationcited by 22
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  5. 2024Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inferencecited by 6
  6. 2025Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Accelerationcited by 5
  7. 2022Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networkscited by 3
  8. 2025Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Acceleratorcited by 3
  9. 2025STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Supportcited by 3
  10. 2025P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formatscited by 3
  11. 2022QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Explorationcited by 2
  12. 2022Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applicationscited by 1
15 more
  1. 2023Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplicationcited by 1
  2. 2024Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensorscited by 1
  3. 2025VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Acceleratorcited by 1
  4. 2025OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloadscited by 1
  5. 2022Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Acceleratorcited by 0
  6. 2022COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networkscited by 0
  7. 2025Energy Efficient Exact and Approximate Systolic Array Architecture for Matrix Multiplicationcited by 0
  8. 2025DISCA: A Digital In-memory Stochastic Computing Architecture Using A Compressed Bent-Pyramid Formatcited by 0
  9. 2025GAVINA: flexible aggressive undervolting for bit-serial mixed-precision DNN accelerationcited by 0
  10. 2026REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligencecited by 0
  11. 2026ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Accelerationcited by 0
  12. 2026CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computationcited by 0
  13. 2026HiKV: Hierarchical Importance-Aware KV Cache with Hardware Acceleration for LLM Decodingcited by 0
  14. 2026Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inferencecited by 0
  15. 2026NOVA: Technology-Architecture Co-Design of Near-Memory Processing for Attention-SSM-MoE Hybrid LLM Inferencecited by 0