Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices

This paper addresses the problem of accelerating large artificial neural networks (ANN), whose topology and weights can evolve via the use of a genetic algorithm. The proposed digital hardware architecture is capable of processing any evolved network topology, whilst at the same time providing a good trade off between throughput, area and power consumption. The latter is vital for a longer battery life on mobile devices. The architecture uses multiple parallel arithmetic units in each processing element (PE). Memory partitioning and data caching are used to minimise the effects of PE pipeline stalling. A first order minimax polynomial approximation scheme, tuned via a genetic algorithm, is used for the activation function generator. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design

High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility

MGSim - Simulation tools for multi-core processor architectures

MGSim is an open source discrete event simulator for on-chip hardware components, developed at the University of Amsterdam. It is intended to be a research and teaching vehicle to study the fine-grained hardware/software interactions on many-core and hardware multithreaded processors. It includes support for core models with different instruction sets, a configurable multi-core interconnect, multiple configurable cache and memory models, a dedicated I/O subsystem, and comprehensive monitoring and interaction facilities. The default model configuration shipped with MGSim implements Microgrids, a many-core architecture with hardware concurrency management. MGSim is furthermore written mostly in C++ and uses object classes to represent chip components. It is optimized for architecture models that can be described as process networks.Comment: 33 pages, 22 figures, 4 listings, 2 table

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

L'abstract è presente nell'allegato / the abstract is in the attachmen

Pairing Software-Managed Caching with Decay Techniques to Balance Reliability and Static Power in Next-Generation Caches

Since array structures represent well over half the area and transistors on-chip, maintaining their ability to scale is crucial for overall technology scaling. Shrinking transistor sizes are resulting in increased probabilities of single events causing single- and multi-bit upsets which require adoption of more complex and power hungry error detection and correction codes (ECC) in hardware. At the same time, SRAM leakage energy is increasing partly due to technology trends and partly due to the increasing number of transistors present. This paper proposes and evaluates methods of reducing the static power requirements of caches, while also maintaining high reliability. In particular, we propose methods of applying reduced ECC techniques to data that has been identified (by programmer or compiler) as error-tolerant. This segregation, in turn, makes both the default data and the error-tolerant data more amenable to decay-based techniques for leakage control. We examine the potential of this split memory hierarchy along several dimensions. In particular, we consider the power and reliability issues inherent in the approach. Overall, we show that our approach allows the ECC requirements of future applications and caches to be met while also reducing leakage energy