Domain-specific Hardware Acceleration for Model Predictive Path Integral Control

Accurately controlling a robotic system in real time is a challenging problem. To address this, the robotics community has adopted various algorithms, such as Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) control. The first is difficult to implement on non-linear systems such as unmanned aerial vehicles, whilst the second requires a heavy computational load. GPUs have been successfully used to accelerate MPPI implementations; however, their power consumption is often excessive for autonomous or unmanned targets, especially when battery-powered. On the other hand, custom designs, often implemented on FPGAs, have been proposed to accelerate robotic algorithms while consuming considerably less energy than their GPU (or CPU) implementation. However, no MPPI custom accelerator has been proposed so far. In this work, we present a hardware accelerator for MPPI control and simulate its execution. Results show that the MPPI custom accelerator allows more accurate trajectories than GPU-based MPPI implementations.

PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution

Deep neural networks (DNNs) have become ubiquitous thanks to their remarkable ability to model complex patterns across various domains such as computer vision, speech recognition, robotics, etc. While large DNN models are often more accurate than simpler, lightweight models, they are also resource- and energy-hungry. Hence, it is imperative to design methods to reduce reliance on such large models without significant degradation in output accuracy. The high computational cost of these models is often necessary only for a reduced set of challenging inputs, while lighter models can handle most simple ones. Thus, carefully combining properties of existing DNN models in a dynamic, input-based way opens opportunities to improve efficiency without impacting accuracy. In this work, we introduce PERTINENCE, a novel online method designed to analyze the complexity of input features and dynamically select the most suitable model from a pre-trained set to process a given input effectively. To achieve this, we employ a genetic algorithm to explore the training space of an ML-based input dispatcher, enabling convergence towards the Pareto front in the solution space that balances overall accuracy and computational efficiency. We showcase our approach on state-of-the-art Convolutional Neural Networks (CNNs) trained on the CIFAR-10 and CIFAR-100, as well as Vision Transformers (ViTs) trained on TinyImageNet dataset. We report results showing PERTINENCE's ability to provide alternative solutions to existing state-of-the-art models in terms of trade-offs between accuracy and number of operations. By opportunistically selecting among models trained for the same task, PERTINENCE achieves better or comparable accuracy with up to 36% fewer operations.