Venue

Date

Share

Efficient Real-Time Inference in Temporal Convolution Networks

Piyush Khandelwal

James MacGlashan

Pete Wurman

Peter Stone

ICRA-2021

2021

Abstract

It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convolution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real-time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.

Related Publications

Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning

ICML, 2025
Siddhant Agarwal*, Harshit Sikchi, Peter Stone, Amy Zhang*

Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as ``zero-shot learning," this ability remains elusive for general-purpose reinforcement learning algorithms. While rec…

Hyperspherical Normalization for Scalable Deep Reinforcement Learning

ICML, 2025
Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo

Scaling up the model size and computation has brought consistent performance improvements in supervised learning. However, this lesson often fails to apply to reinforcement learning (RL) because training the model on non-stationary data easily leads to overfitting and unstab…

A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7

RA-L, 2025
Hojoon Lee, Takuma Seno, Jun Jet Tai, Kaushik Subramanian, Kenta Kawamoto, Peter Stone, Peter R. Wurman

Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require instrumentation external to a car, such as precise localization of agents and opponents, limiting …

  • HOME
  • Publications
  • Efficient Real-Time Inference in Temporal Convolution Networks

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.