The implementation of Prolog via VAX 8600 microcode

Software defined radio (SDR) is characterized by its flexibility and convenience of modification, because significant amounts of signal processing are handed over to the general-purpose processor (GPP), rather than being done in special-purpose hardware. In the long term, software-defined radios work well in low data transmission rate systems such as 2G/3G cellular systems and other wireless radio systems. With rapid development of GPP power, more complex algorithms and higher data rate wireless communication systems can be implemented with GPP processor and general purpose RF front end. In this paper we propose a LTE physical layer parallel system architecture based on multicore GPP to match up the high capacity requests of LTE air interface. Firstly, we analyzed the complexity of each LTE physical layer module and made a statistics of their processing time. And then we used Linux CPU affinity to allocate each LTE module to particular CPU core carefully based on the statistics. CPU utilization rate shows that using multi-core makes the system to have enough capacity. Our system can only match up 2M bandwidth when the LTE physical layer procedures are running on single CPU core, while it can match up 5M bandwidth when it's running on multi-core.

Mobile devices execute applications with diverse compute and performance demands. This paper proposes a general purpose processor that adapts the underlying hardware to a given workload. Existing mobile processors need to utilize more complex heterogeneous substrates to deliver the demanded performance. They incorporate different cores and specialized accelerators. On the contrary, our processor utilizes only modest homogeneous cores and dynamically provides an execution substrate suitable to accelerate a particular workload. Instead of incorporating accelerators, the processor reconfigures one or more cores into accelerators on-the-fly. It improves performance with minimal hardware additions. The accelerators are made of general purpose ALUs reconfigured into a compute fabric and the general purpose pipeline that streams data through the fabric. To enable reconfiguration of ALUs into the fabric, the floorplan of a 4-core processor is changed to place the ALUs in close proximity on the chip. A configurable switched network is added to couple and dynamically reconfigure the ALUs to perform computation of frequently repeated regions, instead of executing general purpose instructions. Through this reconfiguration, the mobile processor specializes its substrate for a given workload and maximizes performance of the existing resources. Our results show that reconfiguration accelerates a set of selected compute intensive workloads by 1.56×, 2,39×, 3,51×, when configuring the accelerator of 1-, 2-, or 4- cores respectively.

We have proposed and evaluated a scalable architecture for implementing multi-gigabit protocol engines. The architecture utilizes a combination of custom-made VLSI circuitry and a general-purpose processor, such as the Intel 960 or the IBM PowerPC 403. Time critical operations such as line coding/decoding, CRC generation/checking, context-independent header processing, and buffer management are implemented in the customized VLSI part. These designs are never-the-less scalable and can be cascaded to further increase throughput. Some of the packet level processing, such as context-dependent header processing, are performed by the general-purpose processor. As processing power increases, more and more functions can be included in the general purpose processor. The throughput of this architecture is shown to be adequate for the operations and bit rates currently specified by Fibre Channel. Future CMOS technology advances will have the potential to further improve the raw throughput.

This paper analyses the performance of the state-of-the-art media ISA (instruction set architecture) extensions in a general-purpose processor, when executing a video encoder based on an affine motion model. In addition to SIMD (single instruction multiple data) fixed-point instructions, these ISA extensions include SIMD floating-point instructions, special-purpose SIMD fixed-point instructions, and cacheability control instructions. In this study, eight time-consuming kernels of the video encoder were hand-optimized, using instructions in all four instruction categories of these media ISA extensions (the FLP version). These kernels were also hand-optimized using only SIMD fixed-point ISA extensions, without special-purpose instructions (the FXP version). For the FLP version, this study resulted in an average kernel-level speedup of 1.37X and an application-level speedup of 1.11X, compared to the FXP version, and an application-level speedup of 3.41X, compared to the C version.

New media and signal processing applications demand ever higher performance while operating within the tight power constraints of mobile devices. A range of hardware implementations is available to deliver computation with varying degrees of area and power efficiency, from general-purpose processors to application-specific integrated circuits (ASICs). The tradeoff of moving towards more efficient customized solutions such as ASICs is the lack of flexibility in terms of hardware reusability and programmability. In this paper, we propose a customized semi-programmable loop accelerator architecture that exploits the efficiency gains available through high levels of customization, while maintaining sufficient flexibility to execute multiple similar loops. A customized instance of the loop accelerator architecture is generated for a particular loop and then the data and control paths are proactively generalized in an efficient manner to increase flexibility. A compiler mapping phase is then able to map other loops onto the same hardware. The efficiency of the programmable accelerator is compared with non-programmable accelerators and with the OpenRISC 1200 general purpose processor. The programmable accelerator is able to achieve up to 34x better power efficiency and 30x better area efficiency than a simple general purpose processor, while trading off as little as 2x power and area efficiency to the non-programmable accelerator.

In this paper, we present a quantitative study that investigates implementation of a layer-2 switching application on a general purpose processor (GPP). The objective is to better understand the main challenges and tradeoffs in using such processors for packet processing applications. The goal of this study is to identify the architectural guidelines for successful development of an application specific instruction processor (ASIP) for such applications. To asses the performance of switching of packets with various lengths, a LEON2 RISC processor has been chosen as a GPP. The obtained results are compared together based on detailed instruction level profiling of the mentioned application.

We present experimental results on FPGA use in special and general purpose processors, using as case studies a computational accelerator for gene sequence analysis, an integer implementation of the DLX microprocessor and a real-time signal processor for rocket telemetry. All these devices have been successfully prototyped, and are now completely functional. We present detailed analysis of our experience with FPGAs in these machines, describing savings in chip count, power consumption, area, and cost. For all quantitites except cost, measured savings were typically an order of magnitude improvement over discrete IC implementations. References

Parallel implementations of Brunotte’s algorithm

Many problems involving network design can be found in the real world, such as electric power circuit planning, telecommunications and phylogenetic trees. In general, solutions for these problems are modeled as forests represented by a graph manipulating thousands or millions of input variables, making it hard to obtain the solutions in a reasonable time. To overcome this restriction, Evolutionary Algorithms (EAs) with dynamic data structures (encodings) have been widely investigated to increase the performance of EAs for Network Design Problems (NDPs). In this context, this paper proposes a parallelization of the node-depth encoding (NDE), a data structure especially designed for NDPs. Based on the NDE the authors have developed a parallel algorithm and a hardware architecture implemented on FPGA (Field-Programmable Gate Array), denominated Hardware Parallelized NDE (HP-NDE). The running times obtained in a general purpose processor (GPP) and the HP-NDE are compared. The results show a significant speedup in relation to the GPP solution, solving NDP in a time limited by a constant. Such time upper bound can be satisfied for any size of network until the hardware resources available on the FPGA are depleted. The authors evaluated the HP-NDE on a Stratix IV FPGA with networks containing up to 2048 nodes.

Collaboration of reconfigurable processors in grid computing: Theory and application

This thesis work presents the implementation and validation of image processing problems in hardware to estimate the performance and precision gain. It compares the implementation for the addressed problem on a Field Programmable Gate Array (FPGA) with a software implementation for a General Purpose Processor (GPP) architecture. For both solutions the implementation costs for their development is an important aspect in the validation. The analysis of the flexibility and extendability that can be achieved by a modular implementation for the FPGA design was another major aspect. This work is based upon approaches from previous work, which included the detection of Binary Large OBjects (BLOBs) in static images and continuous video streams [13, 15]. One addressed problem of this work is the tracking of the detected BLOBs in continuous image material. This has been implemented for the FPGA platform and the GPP architecture. Both approaches have been compared with respect to performance and precision. This research project is motivated by the MI6 project of the Computer Vision research group, which is located at the Bonn-Rhein-Sieg University of Applied Sciences. The intent of the MI6 project is the tracking of a user in an immersive environment. The proposed solution is to attach a light emitting device to the user for tracking the created light dots on the projection surface of the immersive environment. Having the center points of those light dots would allow the estimation of the user’s position and orientation. One major issue that makes Computer Vision problems computationally expensive is the high amount of data that has to be processed in real-time. Therefore, one major target for the implementation was to get a processing speed of more than 30 frames per second. This would allow the system to realize feedback to the user in a response time which is faster than the human visual perception. One problem that comes with the idea of using a light emitting device to represent the user, is the precision error. Dependent on the resolution of the tracked projection surface of the immersive environment, a pixel might have a size in cm2. Having a precision error of only a few pixels, might lead to an offset in the estimated user’s position of several cm. In this research work the development and validation of a detection and tracking system for BLOBs on a Cyclone II FPGA from Altera has been realized. The system supports different input devices for the image acquisition and can perform detection and tracking for five to eight BLOBs. A further extension of the design has been evaluated and is possible with some constraints. Additional modules for compressing the image data based on run-length encoding and sub-pixel precision for the computed BLOB center-points have been designed. For the comparison of the FPGA approach for BLOB tracking a similar implementation in software using a multi-threaded approach has been realized. The system can transmit the detection or tracking results on two available communication interfaces, USB and RS232. The analysis of the hardware solution showed a similar precision for the BLOB detection and tracking as the software approach. One problem is the strong increase of the allocated resources when extending the system to process more BLOBs. With one of the applied target platforms, the DE2-70 board from Altera, the BLOB detection could be extended to process up to thirty BLOBs. The implementation of the tracking approach in hardware required much more effort than the software solution. The design of high level problems in hardware for this case are more expensive than the software implementation. The search and match steps in the tracking approach could be realized more efficiently and reliably in software. The additional pre-processing modules for sub-pixel precision and run-length-encoding helped to increase the system’s performance and precision.

Improving performance of codes with large/irregular stride memory access patterns via high performance reconfigurable computers

HiPeC — High Performance Cryptographic Service for Heterogeneous Network-on-Chip Systems

This paper presents the implementation and optimization of the SCME (Spatial Channel Model Extension) on the GPP (General Purpose Processor) platform using SSE (Streaming SIMD Extensions) instructions and multithreading technique. The SCME which is the expansion of the SCM (Spatial Channel Model) introduces the concept of “mid-paths” and can support up to 100 MHz bandwidths in 2~5 GHz frequency range. In this paper we demonstrate detailed computational analysis of the SCME. In order to reduce the latency, it is very significant to optimize the program. Three optimization methods are proposed: SSE instructions, multithreading and linear interpolation algorithm. SSE instructions and multithreading can solve this problem via increasing the computing parallel degree. Linear interpolation algorithm can reduce the complexity effectively by considering the effectiveness of the sampling rate. From the experiment result we can get that the SSE instructions with the help of IPP (Integrated Performance Primitives) library function can reduce the delay to 1/5 when operating in 32-bit floating-point, multithreading can reduce the delay to 2/5, meanwhile linear interpolation algorithm can reduce the delay to 1/20 with large amount data and ration of linear interpolation L=512. When the three optimization methods are utilized simultaneously, the delay can be cut off remarkably compared with no optimization method utilized.

Till recently dedicated hardware has been required to perform real-time MPEG-2 video encoding. However, with increase in clock frequency and the introduction of video-specific instruction sets, general-purpose processors and DSPs can now approximate the function and performance of single-function hardware. In this paper, a software-only MPEG-2(MP@ML) video encoder implementation aimed at real-time operation on embedded platforms and general-purpose processors is described. The main contribution of this work is a set of algorithmic optimizations that significantly reduces the computational complexity of the encoding process while slightly degrading the subjective video quality compared to encoders that are more exhaustive.