Samarjit Chakraborty

Luigi Capogrosso, Enrico Fraccaroli, Giulio Petrozziello et al.

In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. The flexibility that makes DNNs such a pervasive technology comes at a price: the computational requirements preclude their deployment on most of the resource-constrained edge devices available today to solve real-time and real-world tasks. This paper introduces a novel approach to address this challenge by combining the concept of predefined sparsity with Split Computing (SC) and Early Exit (EE). In particular, SC aims at splitting a DNN with a part of it deployed on an edge device and the rest on a remote server. Instead, EE allows the system to stop using the remote server and rely solely on the edge device's computation if the answer is already good enough. Specifically, how to apply such a predefined sparsity to a SC and EE paradigm has never been studied. This paper studies this problem and shows how predefined sparsity significantly reduces the computational, storage, and energy burdens during the training and inference phases, regardless of the hardware platform. This makes it a valuable approach for enhancing the performance of SC and EE applications. Experimental results showcase reductions exceeding 4x in storage and computational complexity without compromising performance. The source code is available at https://github.com/intelligolabs/sparsity_sc_ee.

Luigi Capogrosso, Enrico Fraccaroli, Samarjit Chakraborty et al.

Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for latency-sensitive applications that do not allow the entire DNN to be deployed remotely, while not having sufficient computation bandwidth available locally. In many such embedded systems scenarios, such as those in the automotive domain, computational resource constraints also necessitate Multi-Task Learning (MTL), where the same DNN is used for multiple inference tasks instead of having dedicated DNNs for each task, which would need more computing bandwidth. However, how to partition such a multi-tasking DNN to be deployed within a SC framework has not been sufficiently studied. This paper studies this problem, and MTL-Split, our novel proposed architecture, shows encouraging results on both synthetic and real-world data. The source code is available at https://github.com/intelligolabs/MTL-Split.

Nicholas Carter, Arkaprava Gupta, Prateek Ganguli et al.

Deep Brain Stimulation (DBS) is a highly effective treatment for Parkinson's Disease (PD). Recent research uses reinforcement learning (RL) for DBS, with RL agents modulating the stimulation frequency and amplitude. But, these models rely on biomarkers that are not measurable in patients and are only present in brain-on-chip (BoC) simulations. In this work, we present an RL-based DBS approach that adapts these stimulation parameters according to brain activity measurable in vivo. Using a TD3 based RL agent trained on a model of the basal ganglia region of the brain, we see a greater suppression of biomarkers correlated with PD severity compared to modern clinical DBS implementations. Our agent outperforms the standard clinical approaches in suppressing PD biomarkers while relying on information that can be measured in a real world environment, thereby opening up the possibility of training personalized RL agents specific to individual patient needs.

Friedrich Kruber, Eduardo Sánchez Morales, Samarjit Chakraborty et al.

The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work describes a process to estimate a precise vehicle position from aerial imagery. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose. Two training data sets are employed: The first one is optimized for detecting the test vehicle, while the second one consists of randomly selected images recorded on public roads. To reduce errors, several aspects are accounted for, such as the drone movement and the perspective projection from a photograph. The estimated position is comapared with a reference system installed in the test vehicle. It is shown, that a mean accuracy of 20 cm can be achieved with flight altitudes up to 100 m, Full-HD resolution and a frame-by-frame detection. A reliable position estimation is the basis for further data processing, such as obtaining additional vehicle state variables. The source code, training weights, labeled data and example videos are made publicly available. This supports researchers to create new traffic data sets with specific local conditions.

Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales et al.

The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopic traffic simulation, a clustering technique and a classification technique for the operational phase. The developed simulation tool models each vehicle separately, while maintaining the dependencies between each other. The clustering approach consists of a modified unsupervised Random Forest algorithm to find a data adaptive similarity measure between all scenarios. As part of this, the path proximity, a novel technique to determine a similarity based on the Random Forest algorithm is presented. In the second part of the clustering, the similarities are used to define a set of clusters. In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase. A thresholding technique is described to ensure a certain confidence level for the class assignment. The method is applied for highway scenarios. The results show that the proposed method is an excellent approach to automatically categorize traffic scenarios, which is particularly relevant for testing autonomous vehicle functionality.

Martin Becker, Emanuel Regnath, Samarjit Chakraborty

SPARK 2014 is a modern programming language and a new state-of-the-art tool set for development and verification of high-integrity software. In this paper, we explore the capabilities and limitations of its latest version in the context of building a flight stack for a high-altitude unmanned glider. Towards that, we deliberately applied static analysis early and continuously during implementation, to give verification the possibility to steer the software design. In this process we have identified several limitations and pitfalls of software design and verification in SPARK, for which we give workarounds and protective actions to avoid them. Finally, we give design recommendations that have proven effective for verification, and summarize our experiences with this new language.

Philipp Mundhenk, Andrew Paverd, Artur Mrowca et al.

With the increasing amount of interconnections between vehicles, the attack surface of internal vehicle networks is rising steeply. Although these networks are shielded against external attacks, they often do not have any internal security to protect against malicious components or adversaries who breach the network perimeter. To secure the in-vehicle network, all communicating components must be authenticated, and only authorized components should be allowed to send and receive messages. This is achieved using an authentication framework. Cryptography is widely used to authenticate communicating parties and provide secure communication channels (e.g., Internet communication). However, the real-time performance requirements of in-vehicle networks restrict the types of cryptographic algorithms and protocols that may be used. In particular, asymmetric cryptography is computationally infeasible during vehicle operation. In this work, we address the challenges of designing authentication protocols for automotive systems. We present Lightweight Authentication for Secure Automotive Networks (LASAN), a full lifecycle authentication approach. We describe the core LASAN protocols and show how they protect the internal vehicle network while complying with the real-time constraints and low computational resources of this domain. Unlike previous work, we also explain how this framework can be integrated into all aspects of the automotive lifecycle, including manufacturing, vehicle maintenance, and software updates. We evaluate LASAN in two different ways: First, we analyze the security properties of the protocols using established protocol verification techniques based on formal methods. Second, we evaluate the timing requirements of LASAN and compare these to other frameworks using a new highly modular discrete event simulator for in-vehicle networks, which we have developed for this evaluation.