Reliable Low-Latency and Low-Complexity Viterbi Architectures Benchmarked on ASIC and FPGA

The Viterbi algorithm is commonly applied in a number of sensitive usage models including decoding convolutional codes used in communications such as satellite communication, cellular relay, and wireless local area networks. Moreover, the algorithm has been applied to automatic speech recognition and storage devices. In this thesis, efficient error detection schemes for architectures based on low-latency, low-complexity Viterbi decoders are presented. The merit of the proposed schemes is that reliability requirements, overhead tolerance, and performance degradation limits are embedded in the structures and can be adapted accordingly. We also present three variants of recomputing with encoded operands and its modifications to detect both transient and permanent faults, coupled with signature-based schemes. The instrumented decoder architecture has been subjected to extensive error detection assessments through simulations, and application-specific integrated circuit (ASIC) [32nm library] and field-programmable gate array (FPGA) [Xilinx Virtex-6 family] implementations for benchmark. The proposed fine-grained approaches can be utilized based on reliability objectives and performance/implementation metrics degradation tolerance

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Visual Analysis Algorithms for Embedded Systems

The main contribution of this thesis is the design and development of an optimized framework to realize the deep neural classifiers on the embedded platforms. Deep convolutional networks exhibit unmatched performance in image classification. However, these deep classifiers demand huge computational power and memory storage. That is an issue on embedded devices due to limited onboard resources. The computational demand of neural networks mainly stems from the convolutional layers. A significant improvement in performance can be obtained by reducing the computational complexity of these convolutional layers, making them realizable on embedded platforms. In this thesis, we proposed a CUDA (Compute Unified Device Architecture)-based accelerated scheme to realize the deep architectures on the embedded platforms by exploiting the already trained networks. All required functions and layers to replicate the trained neural networks were implemented and accelerated using concurrent resources of embedded GPU. Performance of our CUDA-based proposed scheme was significantly improved by performing convolutions in the transform domain. This matrix multiplication based convolution was also compared with the traditional approach to analyze the improvement in inference performance. The second part of this thesis focused on the optimization of the proposed framework. The flow of our CUDA-based framework was optimized using unified memory scheme and hardware-dependent utilization of computational resources. The proposed flow was evaluated over three different image classification networks on Jetson TX1 embedded board and Nvidia Shield K1 tablet. The performance of proposed GPU-only flow was compared with its sequential and heterogeneous versions. The results showed that the proposed scheme brought the higher performance and enabled the real-time image classification on the embedded platforms with lesser storage requirements. These results motivated us towards the realization of useful real-time classification and recognition problems on the embedded platforms. Finally, we utilized the proposed framework to realize the neural network-based automatic license plate recognition (ALPR) system on a mobile platform. This highly-precise and computationally demanding system was deployed by simplifying the flow of trained deep architecture developed for powerful desktop and server environments. A comparative analysis of computational complexity, recognition accuracy and inference performance was performed

The study of sequential decoding techniques for spacecraft telemetry systems Final report, 12 Jan. - 12 Jun. 1968

Convolutional encoding-sequential decoding technique for coherent deep space telemetry link and near earth space mission

The design of an asynchronous BCJR/MAP convolutional channel decoder.

The digital design alternative to the everyday synchronous circuit design paradigm is the asynchronous model. Asynchronous circuits are also known as handshaking circuits and they may prove to be a feasible design alternative in the modern digital Very Large Scale Integration (VLSI) design environment. Asynchronous circuits and systems offer the possibility of lower system power requirements, reduced noise, elimination of clock skew and many other benefits. Channel coding is a useful means of eliminating erroneous transmission due to the communication channel\u27s physical limits. Convolutional coding has come to the forefront of channel coding discussions due to the usefulness of turbo codes. The niche market for turbo codes have typically been in satellite communication. The usefulness of turbo codes are now expanding into the next generation of handheld communication products. It is probable that the turbo coding scheme will reside in the next cellular phone one purchases [1]. Turbo coding uses two BCJR decoders in its implementation. The BCJR decoding algorithm was named after its creators Bahl, Cocke, Jelinek, and Raviv (BCJR). The BCJR algorithm is sometimes known as a Maximum Priori Posteriori (MAP) algorithm. This means a very large part of the turbo coding research will encompass the BCJR/MAP decoder and its optimization for size, power and performance. An investigation into the design of a BCJR/MAP convolutional channel decoder will be introduced. This will encompass the use and synthesis of an asynchronous Hardware Definition Language (HDL) called Balsa. The design will be carried through to the gate implementation level. Proper gate level analysis will identify the key metrics that will determine the feasibility of an asynchronous design of that of the everyday clocked paradigm.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .P47. Source: Masters Abstracts International, Volume: 43-05, page: 1782. Adviser: Kemal Tepe. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

The Telecommunications and Data Acquisition Report

Tracking and ground-based navigation; communications, spacecraft-ground; station control and system technology; capabilities for new projects; networks consolidation program; and network sustaining are described