No abstract

Digital beamforming has been widely used in modern medical ultrasound instruments. Flexibility is the key advantage of a digital beamformer over the traditional analog approach. Unlike analog delay lines, digital delay can be programmed to implement new ways of beam shaping and beam steering without hardware modification. Digital beamformers can also be focused dynamically by tracking the depth and focusing the receive beam as the depth increases. By constantly updating an element weight table, a digital beamformer can dynamically increase aperture size with depth to maintain constant lateral resolution and reduce sidelobe noise. Because ultrasound digital beamformers have high I/O bandwidth and processing requirements, traditionally they have been implemented using ASICs or FPGAs that are costly both in time and in money.This paper introduces a sample implementation of a digital beamformer that is programmed in software on a Massively Parallel Processor Array (MPPA) . The system consists of a host PC and a PCI Express-based beamformer accelerator with an Ambric Am2045 MPPA chip and 512 Mbytes of external memory. The Am2045 has 336 asynchronous RISC-DSP processors that communicate through a configurable structure of channels, using a self-synchronizing communication protocol.

This paper introduces a novel computing architecture that can be reconfigured in real time to adapt on demand to multimode sensor platforms' dynamic computational and functional requirements. This 1 teraOPS reconfigurable Massively Parallel Processor Array (MPPA) has 336 32-bit processors. The programmable 32-bit communication fabric provides streamlined inter-processor connections with deterministically high performance. Software programmability, scalability, ease of use, and fast reconfiguration time (ranging from microseconds to milliseconds) are the most significant advantages over FPGAs and DSPs. This paper introduces the MPPA architecture, its programming model, and methods of reconfigurability. An MPPA platform for reconfigurable computing is based on a structural object programming model. Objects are software programs running concurrently on hundreds of 32-bit RISC processors and memories. They exchange data and control through a network of self-synchronizing channels. A common application design pattern on this platform, called a work farm, is a parallel set of worker objects, with one input and one output stream. Statically configured work farms with homogeneous and heterogeneous sets of workers have been used in video compression and decompression, network processing, and graphics applications. INTRODUCTIONMany advanced SIR (Surveillance, Intelligence and Reconnaissance) sensors support multi-mode operation to respond to unique situational demands. For example, the Lockheed Martin AN/SPY-2 is a long-range, 3D, and multi-function radar that is designed for theater ballistic-missile defense and full-area anti-air warfare in addition to short-to medium-range searching and multi-target tracking. The BAE SAMPSON multi-function radar provides weapon-control functions in addition to search and precision tracking of multiple targets. Most advanced E-O (Electro-Optic) systems integrate multiple types of E-O/IR detectors in one sensor package. The Thales SIRIUS long-range IR search and track system provides bispectral panoramic surveillance with simultaneous MWIR-and LWIR-band operation for automatic detection, track initiation, target priority ranking, and tracking. In moving-platform E-O sensor packages, while versatility is as important as it is in the larger systems, the stringent physical constrains of airborne gimbals and reconnaissance pods pose additional weight and size challenges.Historically, each mode of sensor operation required dedicated hardware and software modules. From the system architecture point of view, such worst-case design approaches waste system resources. A large portion of the processing resources sit idle and redundant at any given time. Demands for increases in functional and operational requirements, and decreases in cost, footprint, and power, suggest the need for a hardware processing platform that can be reconfigured on demand to support multi-mode sensor processing. The DoD's JTRS initiative even made reconfigurability a requirement for Software Defined Radio (SDR). SDR...