C-NNAP - A parallel processing architecture for binary neural networks

This paper describes the CNNAP machine, a MIMD implementation of an array of ADAM binary neural networks, primarily designed for image processing. CNNAP comprises an array of VME cards each containing a DSP, SCSI controller, and a new design of the SAT peripheral processor. The SAT processor is a dedicated hardware implemention that performs binary neural network computations. The SAT processor yields a potential speed-up of between 108 times to 182 times that of the current DSP with its dedicated coprocessor. CNNAP in association with the SAT provides a fast, parallel environment for performing binary neural network operations

A 2-D PROCESSOR ARRAY FOR MASSIVELY PARALLEL IMAGE PROCESSING

The concept of introducing image processing logic within the spatial gaps of an array of photodiodes is the key factor behind the presented work. A two-dimensional massively parallel image processing paradigm based on 8X8 pixel neighborhood digital processors has been designed. A low complexity processor array architecture along with its instruction set has been designed and fully verified on a FPGA platform. Various image processing tests have been run on the FPGA platform to demonstrate the functionality of a design that uses 12 parallel processors. The test results indicate that the architecture is scalable to support high frame rates while allowing for flexible processing due to inherent programmability at a high level. The gate level logic synthesis results of the processor targeting a 0.13 μm CMOS technology indicates a low silicon area complexity, allowing for image sensor integration. Adviser: Dr. Sina Balkir, Co-Adviser: Dr. Michael W. Hoffma

PGPG: An Automatic Generator of Pipeline Design for Programmable GRAPE Systems

We have developed PGPG (Pipeline Generator for Programmable GRAPE), a software which generates the low-level design of the pipeline processor and communication software for FPGA-based computing engines (FBCEs). An FBCE typically consists of one or multiple FPGA (Field-Programmable Gate Array) chips and local memory. Here, the term "Field-Programmable" means that one can rewrite the logic implemented to the chip after the hardware is completed, and therefore a single FBCE can be used for calculation of various functions, for example pipeline processors for gravity, SPH interaction, or image processing. The main problem with FBCEs is that the user need to develop the detailed hardware design for the processor to be implemented to FPGA chips. In addition, she or he has to write the control logic for the processor, communication and data conversion library on the host processor, and application program which uses the developed processor. These require detailed knowledge of hardware design, a hardware description language such as VHDL, the operating system and the application, and amount of human work is huge. A relatively simple design would require 1 person-year or more. The PGPG software generates all necessary design descriptions, except for the application software itself, from a high-level design description of the pipeline processor in the PGPG language. The PGPG language is a simple language, specialized to the description of pipeline processors. Thus, the design of pipeline processor in PGPG language is much easier than the traditional design. For real applications such as the pipeline for gravitational interaction, the pipeline processor generated by PGPG achieved the performance similar to that of hand-written code. In this paper we present a detailed description of PGPG version 1.0.Comment: 24 pages, 6 figures, accepted PASJ 2005 July 2

An embedded system supporting dynamic partial reconfiguration of hardware resources for morphological image processing

Processors for high-performance computing applications are generally designed with a focus on high clock rates, parallelism of operations and high communication bandwidth, often at the expense of large power consumption. However, the emphasis of many embedded systems and untethered devices is on minimal hardware requirements and reduced power consumption. With the incessant growth of computational needs for embedded applications, which contradict chip power and area needs, the burden is put on the hardware designers to come up with designs that optimize power and area requirements. This thesis investigates the efficient design of an embedded system for morphological image processing applications on Xilinx FPGAs (Field Programmable Gate Array) by optimizing both area and power usage while delivering high performance. The design leverages a unique capability of FPGAs called dynamic partial reconfiguration (DPR) which allows changing the hardware configuration of silicon pieces at runtime. DPR allows regions of the FPGA to be reprogrammed with new functionality while applications are still running in the remainder of the device. The main aim of this thesis is to design an embedded system for morphological image processing by accounting for real time and area constraints as compared to a statically configured FPGA. IP (Intellectual Property) cores are synthesized for both static and dynamic time. DPR enables instantiation of more hardware logic over a period of time on an existing device by time-multiplexing the hardware realization of functions. A comparison of power consumption is presented for the statically and dynamically reconfigured designs. Finally, a performance comparison is included for the implementation of the respective algorithms on a hardwired ARM processor as well as on another general-purpose processor. The results prove the viability of DPR for morphological image processing applications

Photonic signal processing for inverse synthetic aperture radar imaging.

A robust signal processor, capable of handling a multitude of signal processing functions over a wide instantaneous signal bandwidth is needed for future military systems where shared sensor and signal processing resources will be employed. We investigated a novel 2D acousto-optic (AO) processor capable of real-time multifunction signal processing. We developed the specifications for an optoelectronic ISAR image formation processor for ship imaging based upon the AN/APS-137(H) Airborne Radar. The baseline processor is designed for high resolution imaging- slant range resolution of 0.3 m and cross range resolution of 1.2 m over 30 m X 30 m window. The optical design of the processor is based on an optically efficient, in-line, high stability, 2D interferometer using four acousto-optic devices invested by Riza. We developed specifications for the processor components, including light source, lenses, photodetector array, and Bragg cells including a multichannel Bragg cell to improve processor bandwidth and reduce its size. We showed that all of these components are commercially available. We breadboarded a narrow bandwidth version of the processor and demonstrated its main operation features. We showed experimentally that the processor has simultaneous spatial carrier generation capability that is controllable with high phase stability and excellent fringe visibility

Configurable 3D-integrated focal-plane sensor-processor array architecture

A mixed-signal Cellular Visual Microprocessor architecture with digital processors is described. An ASIC implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face type integration, and one or several cascaded array of mainly identical (SIMD) processing elements. The individual array elements derived from the same general HDL description and could be of different in size, aspect ratio, and computing resources

Analog multiply and accumulate FPA readout circuit with digital multiply and sign maintenance

The high bandwidth and power needed to process the data coming from modern high resolution focal plane arrays leads to the necessity for fast and efficient read out and data processing. A system that performs block recognition and image classification with efficiency and low latency is presented. The system is comprised of an analog signal processor that will be integrated into the read out integrated circuit. This enables the capability to read out the focal plane array information and process it completely in the analog domain in a comparably very small amount of operational steps. The steps and techniques of the design flow, including definition of problem, concepts and design of system architecture, simulation of system, and analog lay out practices are covered

Digital implementation of the cellular sensor-computers

Two different kinds of cellular sensor-processor architectures are used nowadays in various applications. The first is the traditional sensor-processor architecture, where the sensor and the processor arrays are mapped into each other. The second is the foveal architecture, in which a small active fovea is navigating in a large sensor array. This second architecture is introduced and compared here. Both of these architectures can be implemented with analog and digital processor arrays. The efficiency of the different implementation types, depending on the used CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use digital implementation rather than analog

FMCW rail-mounted SAR: Porting spotlight SAR imaging from MATLAB to FPGA

In this work, a low-cost laptop-based radar platform derived from the MIT open courseware has been implemented. It can perform ranging, Doppler measurement and SAR imaging using MATLAB as the processor. In this work, porting the signal processing algorithms onto a FPGA platform will be addressed as well as differences between results obtained using MATLAB and those obtained using the FPGA platform. The target FPGA platforms were a Virtex6 DSP kit and Spartan3A starter kit, the latter was also low-cost to further reduce the cost for students to access radar technology