Exploiting Posit Arithmetic for Deep Neural Networks in Autonomous Driving Applications

This paper discusses the introduction of an integrated Posit Processing Unit (PPU) as an alternative to Floating-point Processing Unit (FPU) for Deep Neural Networks (DNNs) in automotive applications. Autonomous Driving tasks are increasingly depending on DNNs. For example, the detection of obstacles by means of object classification needs to be performed in real-time without involving remote computing. To speed up the inference phase of DNNs the CPUs on-board the vehicle should be equipped with co-processors, such as GPUs, which embed specific optimization for DNN tasks. In this work, we review an alternative arithmetic that could be used within the co-processor. We argue that a new representation for floating point numbers called Posit is particularly advantageous, allowing for a better trade-off between computation accuracy and implementation complexity. We conclude that implementing a PPU within the co-processor is a promising way to speed up the DNN inference phase

Towards Reliable Multisensory Perception and Its Automotive Applications

Autonomous driving poses numerous challenging problems, one of which is perceiving and understanding the environment. Since self-driving is safety critical and many actions taken during driving rely on the outcome of various perception algorithms (for instance all traffic participants and infrastructural objects in the vehicle's surroundings must reliably be recognized and localized), thus the perception might be considered as one of the most critical subsystems in an autonomous vehicle. Although the perception itself might further be decomposed into various sub-problems, such as object detection, lane detection, traffic sign detection, environment modeling, etc. In this paper the focus is on fusion models in general (giving support for multisensory data processing) and some related automotive applications such as object detection, traffic sign recognition, end-to-end driving models and an example of taking decisions in multi-criterial traffic situations that are complex for both human drivers and for the self-driving vehicles as well

Machine Learning in Ultra-High Energy (UHE) Neutrino Analysis

The Antarctic Impulsive Transient Antenna (ANITA) is a NASA long-duration balloon experiment with the primary goal of detecting ultra-high-energy (> 1017 eV) neutrinos via the Askaryan Effect. This research investigates the usability of a Convolution Neural Network (CNN), a form of machine learning, in differentiating a form of background noise from the data obtained by ANITA from other types of signals. The background noise events of interest here are “payload blasts,” which are background noise events caused by an unknown object on the ANITA payload. CNN is a technique most commonly used in analyzing visual imagery. It is built on the idea of multilayer perceptron, which is used in classifying nonlinear data. The classification is done by identifying features that are special to the set of events being classified. Both TensorFlow [1] and PyTorch [2] were used to create models that can classify the payload blasts from ANITA data vs. non-payload events. These models however can be extended to classify other events that are of interest. The trained CNN models were able to accurately classify the payload blasts with most models being able to achieve an accuracy of around 98%.College of Engineering Undergraduate Research Scholarship (URS)No embargoAcademic Major: Engineering Physic

Compressed Real Numbers for AI: a case-study using a RISC-V CPU

As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision. Therefore, 16-bit and 8-bit compressed format have attracted considerable attention. In this paper, we focused on two families of formats that have already achieved interesting results in compressing binary32 numbers in machine learning applications, without sensible degradation of the accuracy: bfloat and posit. Even if 16-bit and 8-bit bfloat/posit are routinely used for reducing the storage of the weights/biases of trained DNNs, the inference still often happens on the 32-bit FPU of the CPU (especially if GPUs are not available). In this paper we propose a way to decompress a tensor of bfloat/posits just before computations, i.e., after the compressed operands have been loaded within the vector registers of a vector capable CPU, in order to save bandwidth usage and increase cache efficiency. Finally, we show the architectural parameters and considerations under which this solution is advantageous with respect to the uncompressed one

Machine Learning in Ultra-High Energy (UHE) Neutrino Analysis

The Antarctic Impulsive Transient Antenna (ANITA) is a NASA long-duration balloon experiment with the primary goal of detecting ultra-high-energy (> 1017 eV) neutrinos via the Askaryan Effect. This research investigates the usability of a Convolution Neural Network (CNN), a form of machine learning, in differentiating a form of background noise from the data obtained by ANITA from other types of signals. The background noise events of interest here are “payload blasts,” which are background noise events caused by an unknown object on the ANITA payload. CNN is a technique most commonly used in analyzing visual imagery. It is built on the idea of multilayer perceptron, which is used in classifying nonlinear data. The classification is done by identifying features that are special to the set of events being classified. Both TensorFlow [1] and PyTorch [2] were used to create models that can classify the payload blasts from ANITA data vs. non-payload events. These models however can be extended to classify other events that are of interest. The trained CNN models were able to accurately classify the payload blasts with most models being able to achieve an accuracy of around 98%.College of Engineering Undergraduate Research Scholarship (URS)No embargoAcademic Major: Engineering Physic

Deep Learning Development Environment in Virtual Reality

Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep learning more accessible and intuitive, we have built a virtual reality-based DL development environment. Within our environment, the user can move tangible objects to construct a neural network only using their hands. Our software automatically translates these configurations into a trainable model and then reports its resulting accuracy on a test dataset in real-time. Furthermore, we have enriched the virtual objects with visualizations of the model's components such that users can achieve insight about the DL models that they are developing. With this approach, we bridge the gap between professionals in different fields of expertise while offering a novel perspective for model analysis and data interaction. We further suggest that techniques of development and visualization in deep learning can benefit by integrating virtual reality

Inimeste tuvastamine ning kauguse hindamine kasutades kaamerat ning YOLOv3 tehisnärvivõrku

Inimestega vähemalt samal tasemel keskkonnast aru saamine masinate poolt oleks kasulik paljudes domeenides. Mitmed erinevad sensored aitavad selle ülesande juures, enim on kasutatud kaameraid. Objektide tuvastamine on tähtis osa keskkonnast aru saamisel. Selle täpsus on viimasel ajal palju paranenud tänu arenenud masinõppe meetoditele nimega konvolutsioonilised närvivõrgud (CNN), mida treenitakse kasutades märgendatud kaamerapilte. Monokulaarkaamerapilt sisaldab 2D infot, kuid ei sisalda sügavusinfot. Teisalt, sügavusinfo on tähtis näiteks isesõitvate autode domeenis. Inimeste ohutus tuleb tagada näiteks töötades autonoomsete masinate läheduses või kui jalakäija ületab teed autonoomse sõiduki eest. Antud töös uuritakse võimalust, kuidas tuvastada inimesi ning hinnata nende kaugusi samaaegselt, kasutades RGB kaamerat, eesmärgiga kasutada seda autonoomseks sõitmiseks maastikul. Selleks täiustatakse hetkel parimat objektide tuvastamise konvolutsioonilist närvivõrku YOLOv3 (ingl k. You Only Look Once). Selle töö väliselt on simulatsioonitarkvaradega AirSim ning Unreal Engine loodud lumine metsamaastik koos inimestega erinevates kehapoosides. YOLOv3 närvivõrgu treenimiseks võeti simulatsioonist välja vajalikud andmed, kasutades skripte. Lisaks muudeti närvivõrku, et lisaks inimese asukohta tuvastavale piirikastile väljastataks ka inimese kauguse ennustus. Antud töö tulemuseks on mudel, mille ruutkesmine viga RMSE (ingl k. Root Mean Square Error) on 2.99m objektidele kuni 50m kaugusel, säilitades samaaegselt originaalse närvivõrgu inimeste tuvastamise täpsuse. Võrreldavate meetodite RMSE veaks leiti 4.26m (teist andmestikku kasutades) ja 4.79m (selles töös kasutatud andmestikul), mis vastavalt kasutavad kahte eraldiseisvat närvivõrku ning LASSO meetodit. See näitab suurt parenemist võrreldes teiste meetoditega. Edasisteks eesmärkideks on meetodi treenimine ning testimine päris maailmast kogutud andmetega, et näha, kas see üldistub ka sellistele keskkondadele.Making machines perceive environment better or at least as well as humans would be beneficial in lots of domains. Different sensors aid in this, most widely used of which is monocular camera. Object detection is a major part of environment perception and its accuracy has greatly improved in the last few years thanks to advanced machine learning methods called convolutional neural networks (CNN) that are trained on many labelled images. Monocular camera image contains two dimensional information, but contains no depth information of the scene. On the other hand, depth information of objects is important in a lot of areas related to autonomous driving, e.g. working next to an automated machine, pedestrian crossing a road in front of an autonomous vehicle, etc. This thesis presents an approach to detect humans and to predict their distance from RGB camera for off-road autonomous driving. This is done by improving YOLO (You Only Look Once) v3[1], a state-of-the-art object detection CNN. Outside of this thesis, an off-road scene depicting a snowy forest with humans in different body poses was simulated using AirSim and Unreal Engine. Data for training YOLOv3 neural network was extracted from there using custom scripts. Also, network was modified to not only predict humans and their bounding boxes, but also their distance from camera. RMSE of 2.99m for objects with distances up to 50m was achieved, while maintaining similar detection accuracy to the original network. Comparable methods using two neural networks and a LASSO model gave 4.26m (in an alternative dataset) and 4.79m (with dataset used is this work) RMSE respectively, showing a huge improvement over the baselines. Future work includes experiments with real-world data to see if the proposed approach generalizes to other environments