Video analytics introduce new levels of intelligence to automated scene understanding. Neuromorphic algorithms, such as HMAX, are proposed as robust and accurate algorithms that mimic the processing in the visual cortex of the brain. HMAX, for instance, is a versatile algorithm that can be repurposed to target several visual recognition applications. This paper presents the design and evaluation of hardware accelerators for extracting visual features for universal recognition. The recognition applications include object recognition, face identification, facial expression recognition, and action recognition. These accelerators were validated on a multi-FPGA platform and significant performance enhancement and power efficiencies were demonstrated when compared to CMP and GPU platforms. Results demonstrate as much as 7.6X speedup and 12.8X more power-efficient performance when compared to those platforms.

Gridding is a method of interpolating irregularly sampled data on to a uniform grid and is a critical image reconstruction step in several applications which operate on non-Cartesian sampled data. In this paper, we present an algorithm architecture co-design framework for accelerating gridding using FPGAs. We present a parameterized hardware library for accelerating gridding to support both arbitrary and regular trajectories. We further describe our kernel automation framework which supports several kernel functions through look-up-table (LUT) based Taylor polynomial evaluation. This framework is integrated using an in-house multi-FPGA development platform which provides hardware infrastructure for integrating custom accelerators. Design-space exploration is enabled by an automation flow which allows system generation from an algorithm specification. We further provide several case studies by realizing systems for nonuniform fast Fourier transform (NuFFT) with different parameter sets and porting them on to the BEE3 platform. Results show speedups of more than 16X and 2X over existing CPU and FPGA implementations respectively, and up to 5.5 times higher performance-per-watt over a comparable GPU implementation.

Implementations of neuromorphic algorithms are traditionally implemented on platforms which consume significant power, falling short of their biologically underpinnings. Recent improvements in FPGA technology have led to FPGAs becoming a platform in which these rapidly evolving algorithms can be implemented. Unfortunately, implementing designs on FPGAs still prove challenging for nonexperts, limiting their use in the neuroscience domain. In this paper, a FPGA framework is presented which enables neuroscientists to compose multi-FPGA systems for a cortical object classification model. This is demonstrated by mapping this algorithm onto two distinct platforms providing speedups of up to ~28X over a reference CPU implementation.

Graphics Processing Units (GPUs) offer tremendous computational and processing power. The architecture requires high communication bandwidth and lower latency between computation units and caches. 3D die-stacking technology is a promising approach to meet such requirements. To the best of our knowledge no other study has investigated the implementation of 3D technology in GPUs. In this paper, we study the impact of stacking caches using the 3D technology on GPU performance. We also investigate the benefits of using 3D stacked MRAM on GPUs. Our work includes cost, power, and thermal analysis of the proposed architectural designs. Our results show a 53% geometric mean performance speedup for iso-cycle time architectures and about 19% for iso-cost architectures.

A number of measurement studies have convincingly demonstrated that network traffic can exhibit a noticeable self-similar nature, which has a considerable impact on queuing performance. However, many routing protocols developed for MANETs over the past few years have been primarily designed and analyzed under the assumptions of either CBR or Poisson traffic models, which are inherently unable to capture traffic self-similarity. It is crucial to re-examine the performance properties of MANETs in the context of more realistic traffic models before practical implementation show their potential performance limitations. In an effort towards this end, this paper evaluates the performance of three well-known and widely investigated MANET routing protocols, notably DSR, AODV and OLSR, in the presence of the bursty self-similar traffic. Different performance aspects are investigated including, delivery ratio, routing overhead, throughput and end-to-end delay. Our simulation results indicate that DSR routing protocol performs well with bursty traffic models compared to AODV and OLSR in terms of delivery ratio, throughput and end-to-end delay. On the other hand, OLSR performed poorly in the presence of self-similar traffic at high mobility especially in terms of data packet delivery ratio, routing overhead and delay. As for AODV routing protocol, the results show an average performance, yet a remarkably low and stable end-to-end delay