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Vision and video applications are becoming pervasive in mobile and embedded systems. Consumer wearable devices require capabilities for real-time video analytics and prolonged battery lifetimes, which is further driving the need for innovative system designs with low-power, reliability and high performance. Further, the increasing resolution of image sensors in these mobile systems places an increasing...
Video and image content has begun to play a growing role in many applications, ranging from video games to autonomous self-driving vehicles. In this paper, we present accelerators for gist-based scene recognition, saliency-based attention, and HMAX-based object recognition that have multiple uses and are based on the current understanding of the vision systems found in the visual cortex of the mammalian...
A significant challenge in creating machines with artificial vision is designing systems which can process visual information as efficiently as the brain. To address this challenge, we identify key algorithms which model the process of attention and recognition in the visual cortex of mammals. This paper presents Cover - an FPGA framework for generating systems which can potentially emulate the visual...
We present the design and implementation of a universal, single-bit stream library for accelerating matrix-vector multiplication using FPGAs. Our library handles multiple matrix encodings ranging from dense to multiple sparse formats. A key novelty in our approach is the introduction of a hardware-optimized sparse matrix representation called Compressed Variable-Length Bit Vector (CVBV), which reduces...
Advances in neuroscience have enabled researchers to develop computational models of auditory, visual and learning perceptions in the human brain. HMAX, which is a biologically inspired model of the visual cortex, has been shown to outperform standard computer vision approaches for multi-class object recognition. HMAX, while computationally demanding, can be potentially applied in various applications...
Recently significant advances have been achieved in understanding the visual information processing in the human brain. The focus of this work is on the design of an architecture to support HMAX, a widely accepted model of the human visual pathway. The computationally intensive nature of HMAX and wide applicability in real-time visual analysis application makes the design of hardware accelerators...
Reconfigurable hardware such as FPGAs are being increasingly employed for application acceleration due to their high degree of parallelism, flexibility and power efficiency — factors which are key in the rapidly evolving field of embedded real-time vision. While recent advances in technology have increased the capacity of FPGAs, lack of standard models for developing custom accelerators creates issues...
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...
Applications based on Discrete Fourier Transforms (DFT) are extensively used in several areas of signal and digital image processing. Of particular interest is the two-dimensional (2D) DFT which is more computation- and bandwidth-intensive than the one-dimensional (1D) DFT. Traditionally, a 2D DFT is computed using Row-Column (RC) decomposition, where 1D DFTs are computed along the rows followed by...
We present an FPGA accelerator for the Non-uniform Fast Fourier Transform, which is a technique to reconstruct images from arbitrarily sampled data. We accelerate the compute-intensive interpolation step of the NuFFT Gridding algorithm by implementing it on an FPGA. In order to ensure efficient memory performance, we present a novel FPGA implementation for Geometric Tiling based sorting of the arbitrary...
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