Efficient Real-Time Inference in Temporal Convolution Networks

Piyush Khandelwal

James MacGlashan

Peter 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

Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning

AAAI, 2024
Zizhao Wang*, Caroline Wang*, Xuesu Xiao*, Yuke Zhu*, Peter Stone

Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications. In factored state spaces, one approach towards achieving both goals is …

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents

AAAI, 2024
Arrasy Rahman*, Jiaxun Cui*, Peter Stone

Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse tea…

Learning Optimal Advantage from Preferences and Mistaking it for Reward

AAAI, 2024
W. Bradley Knox*, Stephane Hatgis-Kessell*, Sigurdur Orn Adalgeirsson*, Serena Booth*, Anca Dragan*, Peter Stone, Scott Niekum*

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments---as used in reinforcement learning from human feedback (RLHF)---including those used to fine tune ChatGPT and other contemporary language models. Most recent work o…

  • 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.