PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform

Computing with high-dimensional (HD) vectors, also referred to as $\textit{hypervectors}$, is a brain-inspired alternative to computing with scalars. Key properties of HD computing include a well-defined set of arithmetic operations on hypervectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operations. HD computing is about manipulating and comparing large patterns-binary hypervectors with 10,000 dimensions-making its efficient realization on minimalistic ultra-low-power platforms challenging. This paper describes HD computing's acceleration and its optimization of memory accesses and operations on a silicon prototype of the PULPv3 4-core platform (1.5mm$^2$, 2mW), surpassing the state-of-the-art classification accuracy (on average 92.4%) with simultaneous 3.7$\times$ end-to-end speed-up and 2$\times$ energy saving compared to its single-core execution. We further explore the scalability of our accelerator by increasing the number of inputs and classification window on a new generation of the PULP architecture featuring bit-manipulation instruction extensions and larger number of 8 cores. These together enable a near ideal speed-up of 18.4$\times$ compared to the single-core PULPv3

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations

Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing

This paper presents Mr. Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108-mu W fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) -1 GFlop/s-with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr. Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things

An electronic pan/tilt/zoom camera system

A camera system for omnidirectional image viewing applications that provides pan, tilt, zoom, and rotational orientation within a hemispherical field of view (FOV) using no moving parts was developed. The imaging device is based on the effect that from a fisheye lens, which produces a circular image of an entire hemispherical FOV, can be mathematically corrected using high speed electronic circuitry. An incoming fisheye image from any image acquisition source is captured in memory of the device, a transformation is performed for the viewing region of interest and viewing direction, and a corrected image is output as a video image signal for viewing, recording, or analysis. As a result, this device can accomplish the functions of pan, tilt, rotation, and zoom throughout a hemispherical FOV without the need for any mechanical mechanisms. A programmable transformation processor provides flexible control over viewing situations. Multiple images, each with different image magnifications and pan tilt rotation parameters, can be obtained from a single camera. The image transformation device can provide corrected images at frame rates compatible with RS-170 standard video equipment

Functional requirements document for the Earth Observing System Data and Information System (EOSDIS) Scientific Computing Facilities (SCF) of the NASA/MSFC Earth Science and Applications Division, 1992

Five scientists at MSFC/ESAD have EOS SCF investigator status. Each SCF has unique tasks which require the establishment of a computing facility dedicated to accomplishing those tasks. A SCF Working Group was established at ESAD with the charter of defining the computing requirements of the individual SCFs and recommending options for meeting these requirements. The primary goal of the working group was to determine which computing needs can be satisfied using either shared resources or separate but compatible resources, and which needs require unique individual resources. The requirements investigated included CPU-intensive vector and scalar processing, visualization, data storage, connectivity, and I/O peripherals. A review of computer industry directions and a market survey of computing hardware provided information regarding important industry standards and candidate computing platforms. It was determined that the total SCF computing requirements might be most effectively met using a hierarchy consisting of shared and individual resources. This hierarchy is composed of five major system types: (1) a supercomputer class vector processor; (2) a high-end scalar multiprocessor workstation; (3) a file server; (4) a few medium- to high-end visualization workstations; and (5) several low- to medium-range personal graphics workstations. Specific recommendations for meeting the needs of each of these types are presented

On-Line Virtual Real Time E-Collaboration: An Innovative Case Study On Research Teleconferencing Management

The Internet has greatly extended the boundaries of human interaction. Virtual on-line interactions and collaborations via the World Wide Web have become very common place in recent times. It was only a matter of time before these technologies were applied to knowledge sharing and management. In this paper, we discuss the CISSE International Online E-Conference, the World's first and only high caliber scientific conference to be completely conducted and managed online. CISSE demonstrates an innovative approach for conducting and managing an engineering research conference via the internet. CISSE provides a very convenient, time saving and user friendly platform for global scientific and research interactions.http://www.online-journals.org/index.php/i-joe/article/view/67

The JPL telerobotic Manipulator Control and Mechanization (MCM) subsystem

The Manipulator Control and Mechanization (MCM) subsystem of the telerobot system provides the real-time control of the robot manipulators in autonomous and teleoperated modes and real time input/output for a variety of sensors and actuators. Substantial hardware and software are included in this subsystem which interfaces in the hierarchy of the telerobot system with the other subsystems. The other subsystems are: run time control, task planning and reasoning, sensing and perception, and operator control subsystem. The architecture of the MCM subsystem, its capabilities, and details of various hardware and software elements are described. Important improvements in the MCM subsystem over the first version are: dual arm coordinated trajectory generation and control, addition of integrated teleoperation, shared control capability, replacement of the ultimate controllers with motor controllers, and substantial increase in real time processing capability

Robust Intelligent Sensing and Control Multi Agent Analysis Platform for Research and Education

The aim of this thesis is the development and implementation of a controlled testing platform for the Robust Intelligent Sensing and Controls (RISC) Lab at Utah State University (USU). This will be an open source adaptable expandable robotics platform usable for both education and research. This differs from the many other platforms developed in that the entire platform software will be made open source. This open source software will encourage collaboration among other universities and enable researchers to essentially pick up where others have left off without the necessity of replicating months or even years of work. The expected results of this research will create a foundation for diverse robotics investigation at USU as well as enable attempts at novel methods of control, estimation and optimization. This will also contribute a complete software testbed setup to the already vibrant robotics open source research community. This thesis first outlines the platform setup and novel developments therein. The second stage provides an example of how this has been used in education, providing an example curriculum implementing modern control techniques. The third section provides some exploratory research in trajectory control and state estimation of the tip of an inverted pendulum atop a small unmanned aerial vehicle as well as bearing-only cooperative localization experimentation. Finally, a conclusion and future work is discussed