# LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator

arXiv: https://arxiv.org/abs/2501.10658 · HTML: https://mlsanity.com/paper/2501.10658/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 59 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 29 of 29)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [QuIP: 2-Bit Quantization of Large Language Models With Guarantees](https://mlsanity.com/paper/2307.13304/) (2023)
- [QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks](https://mlsanity.com/paper/2402.04396/) (2024)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [FP8 Formats for Deep Learning](https://mlsanity.com/paper/2209.05433/) (2022)
- [BitNet: Scaling 1-bit Transformers for Large Language Models](https://mlsanity.com/paper/2310.11453/) (2023)
- [SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks](https://mlsanity.com/paper/1708.04485/) (2017)
- [LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models](https://mlsanity.com/paper/2206.09557/) (2022)
- [OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization](https://mlsanity.com/paper/2304.07493/) (2023)
- [LLM-FP4: 4-Bit Floating-Point Quantized Transformers](https://mlsanity.com/paper/2310.16836/) (2023)
- [Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration](https://mlsanity.com/paper/1911.09925/) (2019)
- [Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers](https://mlsanity.com/paper/2103.09301/) (2021)
- [ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization](https://mlsanity.com/paper/2208.14286/) (2022)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [Multiplying Matrices Without Multiplying](https://mlsanity.com/paper/2106.10860/) (2021)
- [NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference](https://mlsanity.com/paper/2112.02191/) (2021)
- [LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup](https://mlsanity.com/paper/2302.03213/) (2023)
- [Boosting Mobile CNN Inference through Semantic Memory](https://mlsanity.com/paper/2112.02644/) (2021)
- [PECAN: A Product-Quantized Content Addressable Memory Network](https://mlsanity.com/paper/2208.13571/) (2022)
- [PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration](https://mlsanity.com/paper/2305.18334/) (2023)
- [RAPIDNN: In-Memory Deep Neural Network Acceleration Framework](https://mlsanity.com/paper/1806.05794/) (2018)
- [Memory-Centric Computing](https://mlsanity.com/paper/2305.20000/) (2023)
- [TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems](https://mlsanity.com/paper/2304.01951/) (2023)

## Built on by (most cited first, 9 of 9)

- [MCBP: A Memory-Compute Efficient LLM Inference Accelerator Leveraging Bit-Slice-enabled Sparsity and Repetitiveness](https://mlsanity.com/paper/2509.10372/) (2025)
- [LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs](https://mlsanity.com/paper/2511.06174/) (2025)
- [Algorithmic Simplification of Neural Networks with Mosaic-of-Motifs](https://mlsanity.com/paper/2602.14896/) (2026)
- [EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture](https://mlsanity.com/paper/2605.24144/) (2026)
- [LOCALUT: Harnessing Capacity-Computation Tradeoffs for LUT-Based Inference in DRAM-PIM](https://mlsanity.com/paper/2604.04523/) (2026)
- [FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression](https://mlsanity.com/paper/2605.04084/) (2026)
- [A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference](https://mlsanity.com/paper/2607.24148/) (2026)
- [pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training](https://mlsanity.com/paper/2602.22592/) (2026)
- [ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA](https://mlsanity.com/paper/2605.01935/) (2026)

## Abstract (arXiv)

The emergence of neural network capabilities invariably leads to a significant surge in computational demands due to expanding model sizes and increased computational complexity. To reduce model size and lower inference costs, recent research has focused on simplifying models and designing hardware accelerators using low-bit quantization. However, due to numerical representation limits, scalar quantization cannot reduce bit width lower than 1-bit, diminishing its benefits. To break through these limitations, we introduce LUT-DLA, a Look-Up Table (LUT) Deep Learning Accelerator Framework that utilizes vector quantization to convert neural network models into LUTs, achieving extreme low-bit quantization. The LUT-DLA framework facilitates efficient and cost-effective hardware accelerator designs and supports the LUTBoost algorithm, which helps to transform various DNN models into LUT-based models via multistage training, drastically cutting both computational and hardware overhead. Additionally, through co-design space exploration, LUT-DLA assesses the impact of various model and hardware parameters to fine-tune hardware configurations for different application scenarios, optimizing performance and efficiency. Our comprehensive experiments show that LUT-DLA achieves improvements in power efficiency and area efficiency with gains of 1.4~7.0× and 1.5~146.1×, respectively, while maintaining only a modest accuracy drop. For CNNs, accuracy decreases by 0.1%~3.1% using the L2 distance similarity, 0.1%~3.4% with the L1 distance similarity, and 0.1%~3.8% when employing the Chebyshev distance similarity. For transformer-based models, the accuracy drop ranges from 1.4% to 3.0%.