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Mostrando ítems 1-10 de 11
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
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
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
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
Optimization based on the minimum maximal k-partial-matching problem of finite states machines with input multiplexing
(Springer, 2022-06-02)
Finite State Machines with Input Multiplexing (FSMIMs) were proposed in previous work as a technique for efficient mapping Finite State Machines (FSMs) into ROM memory. In this paper, we present new contributions to the ...
Artículo
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
(Elsevier, 2020)
Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being ...
Artículo
Methodology for Distributed-ROM-based Implementation of Finite State Machines
(Institute of Electrical and Electronics Engineers, 2020-11-24)
This brief explores the optimization of distributed-ROM-based Finite State Machine (FSM) implementations as an alternative to conventional implementations based on Look-Up Tables (LUTs). In distributed-ROM implementations, ...
Artículo
ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers
(Frontiers Media, 2020-11)
Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order ...
Ponencia
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
(IEEE Xplore, 2020-09)
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called Edge-DRNN designed for portable edge computing. EdgeDRNN adopts the spiking neural network inspired delta network ...