Cesar Carranza

Separable FIR Filtering in FPGA and GPU Implementations: Energy, Performance, and Accuracy Considerations

Digital video processing requires significant hardware resources to achieve acceptable performance. Digital video processing based on dynamic partial reconfiguration (DPR) allows the designers to control resources based on energy, performance, and accuracy considerations. In this paper, we present a dynamically reconfigurable implementation of a 2D FIR filter where the number of coefficients and coefficients values can be varied to control energy, performance, and precision requirements. We also present a high-performance GPU implementation to help understand the trade-offs between these two technologies. Results using a standard example of 2D Difference of Gaussians (DOG) filter indicate that the DPR implementation can deliver real-time performance with energy per frame consumption that is an order of magnitude less than the GPU. On the other hand, at significantly higher energy consumption levels, the GPU implementation can deliver very high performance.

Multiscale AM-FM decompositions with GPU acceleration for diabetic retinopathy screening

A Computer Aided Diagnosis system based on multiscale amplitude-modulation frequency-modulation (AM-FM) methods has been recently developed for discriminating between normal and pathological retinal images. The original Matlab implementation of this system required large amounts of computational time and memory resources that would not permit real-time patient consultation. In this manuscript, we present a new implementation of the multiscale AM-FM decomposition, converted from MATLAB code into C/CUDA (Compute Unified Device Architecture) code, in order to take advantage of the graphics processing units (GPU) to significantly reduce the running time and memory resources.

Computational basis for risk stratification of peripheral neuropathy from thermal imaging

The goal of this paper is to present a computer-based system for analyzing thermal images in the detection of preclinical stages of peripheral neuropathy (PN) or diabetic foot. Today, vibration perception threshold (VPT) and sensory tests with a monofilament are used as simple, noninvasive methods for identifying patients who have lost sensation in their feet. These tests are qualitative and are ineffective in stratifying risk for PN in a diabetic patient. In our system a cold stimulus applied to the foot causes a thermoregulatory and corresponding microcirculation response of the foot. A thermal video monitors the recovery of the microcirculation in the foot plantar. Thermal videos for 8 age-matched subjects were analyzed. Six sites were tracked and an average thermal emittance calculated. Characteristics of the recovery curve were extracted using coefficients from an exponential curve fitting process and compared among subjects. The magnitude of the recovery was significantly different for the two classes of subjects. Our system shows evidence of differences between both groups, which could lead to a quantitative test to screen and diagnose peripheral neuropathy.

Dynamic multiobjective optimization management of the Energy-Performance-Accuracy space for separable 2-D complex filters

We present a dynamic framework for 2D complex filter implementation that is based on a multi-objective optimization scheme that generates Pareto-optimal realizations from the Energy-Performance-Accuracy (EPA) space. The EPA space is created by evaluating the 2D complex filter realizations in terms of their required energy, accuracy, and performance. Dynamic EPA management, carried out via Dynamic Partial Reconfiguration (DPR) and Dynamic Frequency Control, then consists on selecting Pareto-optimal realizations that meet time-varying EPA requirements. We demonstrate dynamic EPA management by applying a complex filter to a standard video sequence.

Fast and Scalable Computation of the Forward and Inverse Discrete Periodic Radon Transform

The discrete periodic radon transform (DPRT) has extensively been used in applications that involve image reconstructions from projections. Beyond classic applications, the DPRT can also be used to compute fast convolutions that avoids the use of floating-point arithmetic associated with the use of the fast Fourier transform. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions and the need for a large number of memory accesses. This paper introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: a parallel array of fixed-point adder trees; circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees; an image block-based approach to DPRT computation that can fit the proposed architecture to available resources; and fast transpositions that are computed in one or a few clock cycles that do not depend on the size of the input image. As a result, for an N × N image (N prime), the proposed approach can compute up to N2 additions per clock cycle. Compared with the previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For example, for a 251×251 image, for approximately 25% fewer flip-flops than required for a systolic implementation, we have that the scalable DPRT is computed 36 times faster. For the fastest case, we introduce optimized just 2N + ⌈log2 N⌉ + 1 and 2N + 3 ⌈log2 N⌉ + B + 2 cycles, architectures that can compute the DPRT and its inverse in respectively, where B is the number of bits used to represent each input pixel. On the other hand, the scalable DPRT approach requires more 1-b additions than for the systolic implementation and provides a tradeoff between speed and additional 1-b additions. All of the proposed DPRT architectures were implemented in VHSIC Hardware Description Language (VHDL) and validated using an Field-Programmable Gate Array (FPGA) implementation.

Quantitative Early Detection of Diabetic Foot

Diabetes afflicts an estimated 171 million people worldwide. Diabetic patients are at risk of a wide range of complications including peripheral neuropathy (or diabetic foot). The condition if left untreated will lead to ulcers and eventually lower extremity amputation. Current existing diagnostic techniques for peripheral neuropathy are mostly qualitative procedures based on patient sensations and exhibit significant inter- and intra-observer differences, and an economical quantitative diagnostic technique is still lacking. A system for quantitative early detection of diabetic peripheral neuropathy has been developed based the thermal response of the feet of diabetic patients following cold stimulus. This paper describes the details of the new system, which includes the following key components: (1) A new protocol of using thermal imaging as functional imaging to measure thermal response. (2) Segmentation and tracking of regions of interest (ROIs) for thermal videos. (3) A novel bio-heat transfer model based on thermoregulation. We also report our preliminary patient studies based on two classifiers, which gave strong evidence that the system can used for early quantitative detection of peripheral neuropathy for diabetics.

The Fast Discrete Periodic Radon Transform for prime sized images: Algorithm, architecture, and VLSI/FPGA implementation

The paper introduces the Fast Discrete Periodic Radon Transform (FDPRT) which represents a new algorithm and associated architecture for computing Discrete Periodic Radon Transforms. For square images of size p × p, p prime, the Discrete Periodic Radon Transform (DPRT) requires p2(p-1) additions for calculating image projections along a minimal number of prime directions. The proposed FDPRT architecture can compute the DPRT in p + 1 + dlog2(p)e clock cycles which represents a significant improvement over p2 + p + 1 clock cycles that corresponds to the fastest, previously-reported implementation. The VHDL code of the FDPRT IP core is available under the reconfigurable computer architecture research link from ivpcl.org.

A scalable architecture for implementing the fast discrete periodic radon transform for prime sized images

The Discrete Periodic Radon Transform (DPRT) has many important applications in image processing that are associated with reconstructing objects from projections (e.g., computed tomography [1]) or image restoration (e.g., [2]). Thus, there is strong interest in the development of fast algorithms and architectures for computing the DPRT. This paper introduces a scalable hardware architecture and associated algorithm for computing the DPRT for prime-sized images. For square images of size N × N, N prime, the DPRT requires N2 (N - 1) additions for calculating image projections along a minimal number of prime directions. The proposed approach can compute the DPRT in [N/2h] N + 2N + h clock cycles, h = 1, ..., [log2 N], where h is a scaling factor that is used to control the required hardware resources that are needed to implement the fast DPRT. Compared to previous approaches, a fundamental contribution of the proposed architecture is that it allows effective implementations based on different constraints on the resources.