Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms
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 …
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
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 …
2 form-factor, revolve around safely interacting with humans in complex scenarios, for …
Recent developments in low-power AI accelerators: A survey
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 …
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
Resistive-random-access-memory (ReRAM) based processing-in-memory (R2PIM)
accelerators show promise in bridging the gap between Internet of Thing devices' …
accelerators show promise in bridging the gap between Internet of Thing devices' …
Archytas: A framework for synthesizing and dynamically optimizing accelerators for robotic localization
Despite many recent efforts, accelerating robotic computing is still fundamentally
challenging for two reasons. First, robotics software stack is extremely complicated …
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
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 …
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
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously
increased demand for DNN accelerators. However, designing DNN accelerators is non …
increased demand for DNN accelerators. However, designing DNN accelerators is non …
Smartexchange: Trading higher-cost memory storage/access for lower-cost computation
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 …
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
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 …
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
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 …
of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano …