mlsanityData to 7 Oct 2026

FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism

arXiv 2102.12103 ↗First posted 24 Feb 2021cs.AR cs.LGcited by 5

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Abstract (arXiv)

In this paper, we present a deep reinforcement learning platform named FIXAR which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design approach. Starting from 32-bit fixed-point data, Quantization-Aware Training (QAT) reduces its data precision based on the range of activations and performs retraining to minimize the reward degradation. FIXAR proposes the adaptive array processing core composed of configurable processing elements to support both intra-layer parallelism and intra-batch parallelism for high-throughput inference and training. Finally, FIXAR was implemented on Xilinx U50 and achieves 25293.3 inferences per second (IPS) training throughput and 2638.0 IPS/W accelerator efficiency, which is 2.7 times faster and 15.4 times more energy efficient than those of the CPU-GPU platform without any accuracy degradation.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 24 Feb 2021.

Builds on

12 of 20 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  5. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  6. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  7. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  8. 2016DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradientscited by 557
  9. 2018Distributed Distributional Deterministic Policy Gradientscited by 195
  10. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 178
  11. 2019Quantization Networkscited by 151
  12. 2015Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Controlcited by 33

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2024The Impact of Quantization and Pruning on Deep Reinforcement Learning Modelscited by 6
  2. 2023Number Systems for Deep Neural Network Architectures: A Surveycited by 4
  3. 2022LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learningcited by 1
  4. 2025HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimationcited by 1
  5. 2026AP-DRL: A Synergistic Algorithm-Hardware Framework for Automatic Task Partitioning of Deep Reinforcement Learning on Versal ACAPcited by 0