# Flashlight: Enabling Innovation in Tools for Machine Learning

arXiv: https://arxiv.org/abs/2201.12465 · HTML: https://mlsanity.com/paper/2201.12465/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 67 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Beyond Data and Model Parallelism for Deep Neural Networks](https://mlsanity.com/paper/1807.05358/) (2018)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://mlsanity.com/paper/1911.08460/) (2019)
- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020)
- [Dissecting the Graphcore IPU Architecture via Microbenchmarking](https://mlsanity.com/paper/1912.03413/) (2019)
- [Wav2Letter: an End-to-End ConvNet-based Speech Recognition System](https://mlsanity.com/paper/1609.03193/) (2016)
- [wav2letter++: The Fastest Open-source Speech Recognition System](https://mlsanity.com/paper/1812.07625/) (2018)
- [Chainer: A Deep Learning Framework for Accelerating the Research Cycle](https://mlsanity.com/paper/1908.00213/) (2019)
- [A Fully Differentiable Beam Search Decoder](https://mlsanity.com/paper/1902.06022/) (2019)
- [MIOpen: An Open Source Library For Deep Learning Primitives](https://mlsanity.com/paper/1910.00078/) (2019)

## Built on by (most cited first, 25 of 25)

- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment](https://mlsanity.com/paper/2302.06117/) (2023)
- [Scaling A Simple Approach to Zero-Shot Speech Recognition](https://mlsanity.com/paper/2407.17852/) (2024)
- [NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding](https://mlsanity.com/paper/2505.22857/) (2025)
- [Augmenting Automatic Speech Recognition Models with Disfluency Detection](https://mlsanity.com/paper/2409.10177/) (2024)
- [EURO: ESPnet Unsupervised ASR Open-source Toolkit](https://mlsanity.com/paper/2211.17196/) (2022)
- [AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2309.17395/) (2023)
- [Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions](https://mlsanity.com/paper/2302.04975/) (2023)
- [GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech Recognition](https://mlsanity.com/paper/2311.04996/) (2023)
- [Blank Collapse: Compressing CTC emission for the faster decoding](https://mlsanity.com/paper/2210.17017/) (2022)
- [RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition](https://mlsanity.com/paper/2305.17782/) (2023)
- [On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition](https://mlsanity.com/paper/2407.21476/) (2024)
- [LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration](https://mlsanity.com/paper/2412.15299/) (2024)
- [Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos](https://mlsanity.com/paper/2307.03200/) (2023)
- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)
- [Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach](https://mlsanity.com/paper/2505.24721/) (2025)
- [FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities](https://mlsanity.com/paper/2508.07315/) (2025)
- [Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models](https://mlsanity.com/paper/2210.06936/) (2022)
- [Unsupervised ASR via Cross-Lingual Pseudo-Labeling](https://mlsanity.com/paper/2305.13330/) (2023)
- [Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition](https://mlsanity.com/paper/2401.15532/) (2024)
- [What happens to diffusion model likelihood when your model is conditional?](https://mlsanity.com/paper/2409.06364/) (2024)
- [Analyzing the Importance of Blank for CTC-Based Knowledge Distillation](https://mlsanity.com/paper/2506.01503/) (2025)
- [Unified Learnable 2D Convolutional Feature Extraction for ASR](https://mlsanity.com/paper/2509.10031/) (2025)
- [FLToP CTC: Frame-Level Token Pruning via Relative Threshold for Efficient and Memory-Saving Decoding on Diverse Platforms](https://mlsanity.com/paper/2510.09085/) (2025)
- [Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation](https://mlsanity.com/paper/2605.17710/) (2026)

## Abstract (arXiv)

As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging. While computational needs have driven recent compiler, networking, and hardware advancements, utilization of those advancements by machine learning tools is occurring at a slower pace. This is in part due to the difficulties involved in prototyping new computational paradigms with existing frameworks. Large frameworks prioritize machine learning researchers and practitioners as end users and pay comparatively little attention to systems researchers who can push frameworks forward -- we argue that both are equally important stakeholders. We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems by prioritizing open, modular, customizable internals and state-of-the-art, research-ready models and training setups across a variety of domains. Flashlight allows systems researchers to rapidly prototype and experiment with novel ideas in machine learning computation and has low overhead, competing with and often outperforming other popular machine learning frameworks. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together. Flashlight is available at https://github.com/flashlight/flashlight .