CAMAC bulletin: A publication of the ESONE Committee Issue #14 December 1975 [last pub. of series]

CAMAC is a means of interconnecting many peripheral devices through a digital data highway to a data processing device such as a computer

CIRCUITS AND ARCHITECTURE FOR BIO-INSPIRED AI ACCELERATORS

Technological advances in microelectronics envisioned through Moore’s law have led to powerful processors that can handle complex and computationally intensive tasks. Nonetheless, these advancements through technology scaling have come at an unfavorable cost of significantly larger power consumption, which has posed challenges for data processing centers and computers at scale. Moreover, with the emergence of mobile computing platforms constrained by power and bandwidth for distributed computing, the necessity for more energy-efficient scalable local processing has become more significant. Unconventional Compute-in-Memory architectures such as the analog winner-takes-all associative-memory and the Charge-Injection Device processor have been proposed as alternatives. Unconventional charge-based computation has been employed for neural network accelerators in the past, where impressive energy efficiency per operation has been attained in 1-bit vector-vector multiplications, and in recent work, multi-bit vector-vector multiplications. In the latter, computation was carried out by counting quanta of charge at the thermal noise limit, using packets of about 1000 electrons. These systems are neither analog nor digital in the traditional sense but employ mixed-signal circuits to count the packets of charge and hence we call them Quasi-Digital. By amortizing the energy costs of the mixed-signal encoding/decoding over compute-vectors with many elements, high energy efficiencies can be achieved. In this dissertation, I present a design framework for AI accelerators using scalable compute-in-memory architectures. On the device level, two primitive elements are designed and characterized as target computational technologies: (i) a multilevel non-volatile cell and (ii) a pseudo Dynamic Random-Access Memory (pseudo-DRAM) bit-cell. At the level of circuit description, compute-in-memory crossbars and mixed-signal circuits were designed, allowing seamless connectivity to digital controllers. At the level of data representation, both binary and stochastic-unary coding are used to compute Vector-Vector Multiplications (VMMs) at the array level. Finally, on the architectural level, two AI accelerator for data-center processing and edge computing are discussed. Both designs are scalable multi-core Systems-on-Chip (SoCs), where vector-processor arrays are tiled on a 2-layer Network-on-Chip (NoC), enabling neighbor communication and flexible compute vs. memory trade-off. General purpose Arm/RISCV co-processors provide adequate bootstrapping and system-housekeeping and a high-speed interface fabric facilitates Input/Output to main memory

Embedded System Design of Robot Control Architectures for Unmanned Agricultural Ground Vehicles

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly being implemented onto the modular machinery design of agricultural equipment. This work is being pursued in effort to provide firmware descriptions and hardware architectures that integrate cutting edge technology onto the embedded control architectures of agricultural machinery designs to assist in achieving the end goal of complete and reliable unmanned agricultural automation. In this thesis, various types of autonomous control algorithms integrated with obstacle avoidance or guidance schemes, were implemented onto controller area network (CAN) based distributive real-time systems (DRTSs) in form of the two unmanned agricultural ground vehicles (UAGVs). Both vehicles are tailored to different applications in the agriculture domain as they both leverage state-of-the-art sensors and modules to attain the end objective of complete autonomy to allow for the automation of various types of agricultural related tasks. The further development of the embedded system design of these machines called for the developed firmware and hardware to be implemented onto both an event triggered and time triggered CAN bus control architecture as each robot employed its own separate embedded control scheme. For the first UAGV, a multiple GPS waypoint navigation scheme is derived, developed, and evaluated to yield a fully controllable GPS-driven vehicle. Additionally, obstacle detection and avoidance capabilities were also implemented onto the vehicle to serve as a safety layer for the robot control architecture, giving the ground vehicle the ability to reliability detect and navigate around any obstacles that may happen to be in the vicinity of the assigned path. The second UAGV was a smaller robot designed for field navigation applications. For this robot, a fully autonomous sensor based algorithm was proposed and implemented onto the machine. It is demonstrated that the utilization and implementation of laser, LIDAR, and IMU sensors onto a mobile robot platform allowed for the realization of a fully autonomous non-GPS sensor based algorithm to be employed for field navigation. The developed algorithm can serve as a viable solution for the application of microclimate sensing in a field. Advisors: A. John Boye and Santosh Pitl

Embedded System Design of Robot Control Architectures for Unmanned Agricultural Ground Vehicles

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly being implemented onto the modular machinery design of agricultural equipment. This work is being pursued in effort to provide firmware descriptions and hardware architectures that integrate cutting edge technology onto the embedded control architectures of agricultural machinery designs to assist in achieving the end goal of complete and reliable unmanned agricultural automation. In this thesis, various types of autonomous control algorithms integrated with obstacle avoidance or guidance schemes, were implemented onto controller area network (CAN) based distributive real-time systems (DRTSs) in form of the two unmanned agricultural ground vehicles (UAGVs). Both vehicles are tailored to different applications in the agriculture domain as they both leverage state-of-the-art sensors and modules to attain the end objective of complete autonomy to allow for the automation of various types of agricultural related tasks. The further development of the embedded system design of these machines called for the developed firmware and hardware to be implemented onto both an event triggered and time triggered CAN bus control architecture as each robot employed its own separate embedded control scheme. For the first UAGV, a multiple GPS waypoint navigation scheme is derived, developed, and evaluated to yield a fully controllable GPS-driven vehicle. Additionally, obstacle detection and avoidance capabilities were also implemented onto the vehicle to serve as a safety layer for the robot control architecture, giving the ground vehicle the ability to reliability detect and navigate around any obstacles that may happen to be in the vicinity of the assigned path. The second UAGV was a smaller robot designed for field navigation applications. For this robot, a fully autonomous sensor based algorithm was proposed and implemented onto the machine. It is demonstrated that the utilization and implementation of laser, LIDAR, and IMU sensors onto a mobile robot platform allowed for the realization of a fully autonomous non-GPS sensor based algorithm to be employed for field navigation. The developed algorithm can serve as a viable solution for the application of microclimate sensing in a field. Advisors: A. John Boye and Santosh Pitl

Large space structures and systems in the space station era: A bibliography with indexes (supplement 05)

Bibliographies and abstracts are listed for 1363 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and July 31, 1992. Topics covered include technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion and solar power satellite systems

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980