Efficient Real-Time Inference in Temporal Convolution Networks

Piyush Khandelwal

James MacGlashan

Pete Wurman

Peter Stone




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

Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning

AAAI, 2023
Bo Liu*, Yihao Feng*, Qiang Liu*, Peter Stone

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics tasks, sample efficiency is of the utmost importance for GCRL since, by default, the …

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

AAAI, 2023
Serena Booth*, W. Bradley Knox*, Julie Shah*, Scott Niekum*, Peter Stone, Alessandro Allievi*

In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance metric is often sparse. For example, a true task metric might encode a reward of 1 upon success and 0 otherwise. These sparse task metrics can be hard to learn from, so in pr…

DM2: Distributed Multi-Agent Reinforcement Learning via Distribution Matching

AAAI, 2023
Caroline Wang*, Ishan Durugkar*, Elad Liebman*, Peter Stone

Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to centralized components or explicit communic…

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


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.