# The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

arXiv: https://arxiv.org/abs/1911.05289 · HTML: https://mlsanity.com/paper/1911.05289/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 15 of 15)

- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [Searching for Fast Model Families on Datacenter Accelerators](https://mlsanity.com/paper/2102.05610/) (2021)
- [ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training](https://mlsanity.com/paper/2104.11125/) (2021)
- [Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem](https://mlsanity.com/paper/2209.04579/) (2022)
- [Understanding Time Variations of DNN Inference in Autonomous Driving](https://mlsanity.com/paper/2209.05487/) (2022)
- [Merging Two Cultures: Deep and Statistical Learning](https://mlsanity.com/paper/2110.11561/) (2021)
- [Pairing Conceptual Modeling with Machine Learning](https://mlsanity.com/paper/2106.14251/) (2021)
- [Flexible Multiple-Objective Reinforcement Learning for Chip Placement](https://mlsanity.com/paper/2204.06407/) (2022)
- [An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator](https://mlsanity.com/paper/2501.19347/) (2025)
- [A Survey of Deep Learning Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/2112.14911/) (2021)
- [Bitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices](https://mlsanity.com/paper/2311.13502/) (2023)
- [Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge](https://mlsanity.com/paper/2606.19964/) (2026)
- [The Hidden Power of Pure 16-bit Floating-Point Neural Networks](https://mlsanity.com/paper/2301.12809/) (2023)
- [Exploring shared memory architectures for end-to-end gigapixel deep learning](https://mlsanity.com/paper/2304.12149/) (2023)
- [Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain](https://mlsanity.com/paper/2402.06673/) (2024)

## Abstract (arXiv)

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today.