Limited power budgets and the need for high performance computing have led to platform customization with a number of accelerators integrated with CMPs. In order to study customized architectures, we model four customization design points and compare their performance and energy across a number of computer vision workloads. We analyze the limitations of generic architectures and quantify the costs of increasing customization using these micro-architectural design points. This analysis leads us to develop a framework consisting of low-power multi-cores and an array of configurable micro-accelerator functional units. Using this platform, we illustrate dataflow and control processing optimizations that provide for performance gains similar to custom ASICs for a wide range of vision benchmarks.

Harnessing the computational capabilities of dynamical systems has attracted the attention of scientists and engineers form varied technical disciplines over decades. The time evolution of coupled, non-linear synchronous oscillatory systems has led to active research in understanding their dynamical properties and exploring their applications in brain-inspired, neuromorphic computational models. In this paper we present the realization of coupled and scalable relaxation-oscillators utilizing the metal-insulator-metal transition of vanadium-dioxide (VO2) thin films. We demonstrate the potential use of such a system in pattern recognition, as one possible computational model using such a system.

Embedded visual assist systems are emerging as increasingly viable tools for aiding visually impaired persons in their day-to-day life activities. Novel wearable devices with imaging capabilities will be uniquely positioned to assist visually impaired in activities such as grocery shopping. However, supporting such time-sensitive applications on embedded platforms requires an intelligent trade-off between accuracy and computational efficiency. In order to maximize their utility in real-world scenarios, visual classifiers often need to recognize objects within large sets of object classes that are both diverse and deep. In a grocery market, simultaneously recognizing the appearance of people, shopping carts, and pasta is an example of a common diverse object classification task. Moreover, a useful visual-aid system would need deep classification capability to distinguish among the many styles and brands of pasta to direct attention to a particular box. Exemplar Support Vector Machines (ESVMs) provide a means of achieving this specificity, but are resource intensive as computation increases rapidly with the number of classes to be recognized. To maintain scalability without sacrificing accuracy, we examine the use of a biologically-inspired classifier (HMAX) as a front-end filter that can narrow the set of ESVMs to be evaluated. We show that a hierarchical classifier combining HMAX and ESVM performs better than either of the two individually. We achieve 12% improvement in accuracy over HMAX and 4% improvement over ESVM while reducing computational overhead of evaluating all possible exemplars.

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.

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.