Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms

R Eyvazpour, M Shoaran, G Karimian - Artificial Intelligence Review, 2023 - Springer
Simultaneous localization and mapping (SLAM) is an active research topic in machine
vision and robotics. It has various applications in many different fields such as mobile robots …

Fully onboard ai-powered human-drone pose estimation on ultralow-power autonomous flying nano-uavs

D Palossi, N Zimmerman, A Burrello… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm
2 form-factor, revolve around safely interacting with humans in complex scenarios, for …

Recent developments in low-power AI accelerators: A survey

C Åleskog, H Grahn, A Borg - Algorithms, 2022 - mdpi.com
As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …

Timely: Pushing data movements and interfaces in pim accelerators towards local and in time domain

W Li, P Xu, Y Zhao, H Li, Y Xie… - 2020 ACM/IEEE 47th …, 2020 - ieeexplore.ieee.org
Resistive-random-access-memory (ReRAM) based processing-in-memory (R2PIM)
accelerators show promise in bridging the gap between Internet of Thing devices' …

Archytas: A framework for synthesizing and dynamically optimizing accelerators for robotic localization

W Liu, B Yu, Y Gan, Q Liu, J Tang, S Liu… - MICRO-54: 54th Annual …, 2021 - dl.acm.org
Despite many recent efforts, accelerating robotic computing is still fundamentally
challenging for two reasons. First, robotics software stack is extremely complicated …

An open source and open hardware deep learning-powered visual navigation engine for autonomous nano-uavs

D Palossi, F Conti, L Benini - 2019 15th International …, 2019 - ieeexplore.ieee.org
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10
Watts of total power budget, have so far been considered incapable of running sophisticated …

Dnn-chip predictor: An analytical performance predictor for dnn accelerators with various dataflows and hardware architectures

Y Zhao, C Li, Y Wang, P Xu, Y Zhang… - ICASSP 2020-2020 …, 2020 - ieeexplore.ieee.org
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously
increased demand for DNN accelerators. However, designing DNN accelerators is non …

Smartexchange: Trading higher-cost memory storage/access for lower-cost computation

Y Zhao, X Chen, Y Wang, C Li, H You… - 2020 ACM/IEEE 47th …, 2020 - ieeexplore.ieee.org
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-
cost memory storage/access for lower-cost computation, for energy-efficient inference of …

A 65-nm neuromorphic image classification processor with energy-efficient training through direct spike-only feedback

J Park, J Lee, D Jeon - IEEE Journal of Solid-State Circuits, 2019 - ieeexplore.ieee.org
Recent advances in neural network (NN) and machine learning algorithms have sparked a
wide array of research in specialized hardware, ranging from high-performance NN …

Improving autonomous nano-drones performance via automated end-to-end optimization and deployment of dnns

V Niculescu, L Lamberti, F Conti… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion
of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano …