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Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches. 

Abstract Image registration is an inherently ill-posed problem that lacks the constraints needed for a unique mapping between voxels of the two images being registered. As such, one must regularize the registration to achieve physically meaningful transforms. The regularization penalty is usually a function of derivatives of the displacement-vector field and can be calculated either analytically or numerically. The numerical approach, however, is computationally expensive depending on the image size, and therefore a computationally efficient analytical framework has been developed. Using cubic B-splines as the registration transform, we develop a generalized mathematical framework that supports five distinct regularizers: diffusion, curvature, linear elastic, third-order, and total displacement. We validate our approach by comparing each with its numerical counterpart in terms of accuracy. We also provide benchmarking results showing that the analytic solutions run significantly faster—up to two orders of magnitude—than finite differencing based numerical implementations. 

Traditional single-grid and pyramidal B-spline parameterizations used in deformable image registration require users to specify control point spacing configurations capable of accurately capturing both global and complex local deformations. In many cases, such grid configurations are non-obvious and largely selected based on user experience. Recent regularization methods imposing sparsity upon the B-spline coefficients throughout simultaneous multi-grid optimization, however, have provided a promising means of determining suitable configurations automatically. Unfortunately, imposing sparsity on over-parameterized B-spline models is computationally expensive and introduces additional difficulties such as undesirable local minima in the B-spline coefficient optimization process. To overcome these difficulties in determining B-spline grid configurations, this paper investigates the use of convolutional neural networks (CNNs) to learn and infer expressive sparse multi-grid configurations prior to B-spline coefficient optimization. Experimental results show that multi-grid configurations produced in this fashion using our CNN based approach provide registration quality comparable to L1-norm constrained over-parameterizations in terms of exactness, while exhibiting significantly reduced computational requirements. 

In this paper, we present a model to obtain prior knowledge for organ localization in CT thorax images using three dimensional convolutional neural networks (3D CNNs). Specifically, we use the knowledge obtained from CNNs in a Bayesian detector to establish the presence and location of a given target organ defined within a spherical coordinate system. We train a CNN to perform a soft detection of the target organ potentially present at any point, x = [r,Θ,Φ]T. This probability outcome is used as a prior in a Bayesian model whose posterior probability serves to provide a more accurate solution to the target organ detection problem. The likelihoods for the Bayesian model are obtained by performing a spatial analysis of the organs in annotated training volumes. Thoracic CT images from the NSCLC–Radiomics dataset are used in our case study, which demonstrates the enhancement in robustness and accuracy of organ identification. The average value of the detector accuracies for the right lung, left lung, and heart were found to be 94.87%, 95.37%, and 90.76% after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14%, 96.20%, and 95.15%, respectively, showing a marked improvement in heart detection. This workflow improves the detection rate since the decision is made employing both lower level features (edges, contour etc) and complex higher level features (spatial relationship between organs). This strategy also presents a new application to CNNs and a novel methodology to introduce higher level context features like spatial relationship between objects present at a different location in images to real world object detection problems.