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Mostrando ítems 1-10 de 22
Artículo
Efficient Memory Organization for DNN Hardware Accelerator Implementation on PSoC
(MDPI, 2021-01)
The use of deep learning solutions in different disciplines is increasing and their algorithms are computationally expensive in most cases. For this reason, numerous hardware accelerators have appeared to compute their ...
Artículo
Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics
(MDPI, 2018)
Taking inspiration from biology to solve engineering problems using the organizing principles of biological neural computation is the aim of the field of neuromorphic engineering. This field has demonstrated success in ...
Artículo
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
(Cornell University, 2016)
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take ...
Artículo
Mapping Outputs and States Encoding Bits to Outputs Using Multiplexers in Finite State Machine Implementations
(MDPI, 2023-01-18)
This paper proposes a new technique for implementing Finite State Machines (FSMs) in Field Programmable Gate Arrays (FPGAs). The proposed approach extends the called column compaction in two ways. First, it is applied to ...
Artículo
A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach
(IEEE Computer Society, 2017)
This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital ...
Artículo
Interfacing PDM MEMS Microphones with PFM Spiking Systems: Application for Neuromorphic Auditory Sensors
(Springer, 2022)
Neuromorphic computation processes sensors output in the spiking domain, which presents constraints in many cases when converting information to spikes, loosing, as example, temporal accuracy. This paper presents a spike-based ...
Artículo
A New Approach for Implementing Finite State Machines with Input Multiplexing
(MDPI, 2023-09)
The model called Finite State Machine with Input Multiplexing (FSMIM) was proposed as a mechanism for implementing Finite State Machines (FSMs) using ROM memory. This paper presents a novel approach for achieving more ...
Artículo
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
(IEEE Computer Society, 2019)
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many stateof- the-art (SOA) visual processing tasks. Even though Graphical Processing Units (GPUs) are most often ...
Artículo
EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference
(IEEE Xplore, 2020-12)
Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human machine interaction. We propose a lightweight ...
Artículo
Hardware/software codesign of configurable fuzzy control systems
(Elsevier, 2004)
Fuzzy inference techniques are an attractive and well-established approach for solving control problems. This is mainly due to their inherent ability to obtain robust, low-cost controllers from the intuitive (and usually ...