University of Florida / Center for Computer Vision & Visualization

AIM Project - Adaptive Image Manager

Sponsor: DARPA/ITO Adaptive Computing Systems Program

Department of Computer & Information Science & Engineering
University of Florida
P.O. Box 116120
Gainesville, FL 32611-6120

AIM Overview

Tasks performed by image and signal processing (ISP) hardware are growing in complexity due to a nonlinearly increasing image data burden and increased depth of computation incurred by more sophisticated image understanding or compression algorithms. Power and space limitations upon on-board processor design motivate the increasing prevalence of distributed computation for ISP algorithms. In realistic airborne sensing and processing scenarios, processor fault and failure rates can increase due to a variety of hazards such as channel jamming and exposure to various types of radiation. Hence, it is reasonable to expect that fault tolerance should be built into software that maps ISP algorithms to such hardware systems.

In response to this situation, the AIM Project proposes to radically enhance the Parallel Image Manager (PIM) server developed at University of Florida's Center for Computer Vision and Visualization (UF/CCVV), to map image and signal processing (ISP) algorithms around faulting or failing components in a heterogeneous network of processors. All or a portion of such hardware could be reconfigurable SIMD or SMP processors as well as machines based on field-programmable gate arrays (FPGAs). In practice, each device could exhibit idiosyncratic faulting or failing behaviors.

AIM's Status Manager and Multi-Level Debugger would implement fault tolerance by routing the algorithm mapping process around defective hardware. We expect to achieve this goal via a periodically-updated status map that would describe the state of each hardware component in a given computational system. Status updates would be relayed by flexible libraries designed to control and monitor computational hardware.

Advantageous Features. In practice, AIM would have the following practical advantages:

Key features of AIM are Symbolic- and Execution-Level Debuggers that would work together with AIM's Status Manager to determine whether or not incorrect assignments of operations to given hardware components were scheduled. For example, if a 3x3-pixel convolver was suddently unavailable (due to a fault or failure mode), another device with similar capabilities would be located, where available, and would be substituted for the original resource, when available. Meanwhile, computations originally mapped to the convolver would be suspended, thereby implying sophisticated capabilities for managing out-of-order execution. Such capabilities have been developed in part for PIM, and would be greatly enhanced in the AIM system.

Technical Challenges. Expected areas of scientific or technical difficulty include:

AIM Personnel and Partners

AIM Facilities

The Center for Computer Vision and Visualization is one of four research centers at University of Florida's Department of CISE. All UF/CCVV personnel have access to the Departmental network of over 200 Sun and SGI workstations, monochrome and color laser printers, file servers, etc. UF/CCVV is affiliated with UF/CISE's Parallel Research Laboratory (PRL), which has a 64-processor N-Cube MIMD machine, 1,024-processor MasPar MP-2 and 9,120-processor PAL-I SIMD machines.

The PAL processor is the first in a series of fast, compact SIMD machines designed for image and signal processing applications. Developed under the sponsorship of the PAL consortium (USAF Research Laboratory, Lockheed-Martin Corp., and University of Florida), PAL-I is capable of more than 385 MOPs (8-bit operations). PAL-II, which is in fabrication, is expected to achieve peak throughput of 2.5 GOPs (32-bit IEEE floating point operations) in a 4x6-inch form factor.

AIM Project Information

Publically-available project data are grouped as follows:

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