1. Printing: This poster is 48” wide by 36” high. It’s designed to be printed on a large-format printer. Customizing the Content: The placeholders in this poster are formatted for you. Type in the placeholders to add text, or click an icon to add a table, chart, SmartArt graphic, picture or multimedia file. To add or remove bullet points from text, click the Bullets button on the Home tab. If you need more placeholders for titles, content or body text, make a copy of what you need and drag it into place. PowerPoint’s Smart Guides will help you align it with everything else. Want to use your own pictures instead of ours? No problem! Just click a picture, press the Delete key, then click the icon to add your picture. Neuromorphic chips have potential to be used in driverless cars and car safety. The neuromorphic visual information processing offers an alternative which mimics the robustness and flexibility of the primary visual cortex, which is ideal for driverless cars. The elements of implementation of neuromorphic visual system are introduced with the orientation tuned function of synaptic connections and the spiking neurons. Researchers tested the effectiveness of proposed neuromorphic visual processing system and evaluated it to have a 95% pedestrian detection rate. The chip is able to recognize a certain pattern to identify the head of a person and can even do so in extreme conditions such as fog or snow. Memristor Based Neuromorphic Vision Systems Sam Nosenzo and Trevor Williams || Group C11 Neuromorphics: An Introduction Neuromorphic vision is a way for machines to see and understand their environment in much of the same way that mammals do, These machines are able to do this through the implementation of neuromorphic chips. Neuromorphic chips are biologically inspired by the way the brain processes information through its millions of neurons and billions of synapses. In this way, neuromorphic chips are able to process sensory data (such as visual data) in ways that traditional computers can’t. They can do this through the capabilities of memristors, a newfound circuit element that can replicate neurons and synapses in computational architectures. These artificial neural networks are the basis of the chip’s brain-like intuition and utility.. Memristors Memristors are a unique type of circuit element that express the property of memristance. Memristance gives the memristor its ability to increase the resistance when current flows one way, and decrease resistance when current flows the other way. When there is no charge flowing through the circuit the memristor will retain its last resistance. This means that they essentially act like a resistor while also being able to access a history of past voltages. This allows them to have a kind of certain plasticity, making them ideal for simulating neurons in neuromorphic architectures. Neuromorphic Chips Neuromorphic chips are the basis of neuromorphic vision. Their computational architecture is based off of the massively parallel way the brain functions. This arrangement is very different from the traditional, Von Neumann computational architecture, which is sequential. Neuromorphic Vision SystemsFuture Applications A research team from the Singapore Institute for Neurotechnology, called SI42NAPSE, is exploring the use of vision to improve the functionality of active prosthetic arms for amputees by helping the arm to visually recognize and sense nearby objects for manipulation allowing the prosthetic arm to be more spatially aware Conclusion Memristor based neuromorphic chips have potential to be revolutionary. Once fully actualized and implemented, they will allow computers and machines to extrapolate the world as humans do. By including memristors in the neuromorphic chip, the chip is able to function as a neural network, like those in the brain that process information. Through their ability to detect and discern object patterns in images, neuromorphic vision can be used in driverless cars and prosthetic limbs making life easier for users. Neuromorphic chips mimic the brain by being able to store large amounts of information as well as being able to recognize and detect patterns. With the chips ability to learn, previous constraints on the advancement of technology are being overcome. The neuromorphic chip’s ability to remember, recognize patterns, learn, and overall, act as a brain can bring current technology to new heights. As engineers are constantly looking to improve the technology of today, the implementation of neuromorphic chips will lead to many advances in technology from faster computers to safer driverless cars. Neuromorphic chips have the potential to completely blur the lines of difference between biological and mechanical capabilities. Below is an example of output from a neuromorphic object detection algorithm. In yellow boxes are what the computer recognized as humans. In the pink boxes are what the computer classified as bicycles. In the blue boxes are what the computer determined to be cars. Neuromorphic Chips Traditional Computers Traditional Neuromorphic Neuromorphic Chip Object detection and pattern recognition are a huge part of neuromorphic research in vision systems. Since neuromorphic chips are based off of the way neurons are connected in the brain, researchers are able to create systems that process visual information in many similar ways that humans do. Neuromorphic chips are ideal for this application because of their ability to collect and store data while processing it much like the brain. So, in theory it would be able to discern certain objects from their backgrounds and know what they are while doing so. When researchers tested neuromorphic vision using a frameless artificial retina they found it to emulate biological vision much faster than real time, This means that it would be possible for a neuromorphic vision system to search through and extract visual characteristics from an entire image database in just a few seconds. This flow chart shows the process that a neuromorphic object detection algorithm goes through. It first assesses the luminance and contrast of certain areas of the image to detect possible object boundaries. This step is similar to what the retina of the eye does in mammals. The system takes that data and determines objects based on their boundaries and whether the object is moving or not. It then assesses the object’s special presence and classifies the object by matching it to a predetermined pattern. Driverless Cars IBM’s research team came up with numerous possible applications of this neuromorphic technology. One element of the abilities are in an autonomous search and rescue robot. The robot is spherical and covered in dozens of visual and auditory sensors which could allow them to gather information about their environment and navigate it accordingly. There are speakers on the robot that can convey a message to any person who is having difficulty evacuating a disaster area or were in danger. Neuromorphic vision would help the robot navigate and understand its environment. Process Complex Sensory informationProcess numerical calculations and algorithms Energy EfficientSequential Architecture Parallel Architecture – allows processes to happen simultaneously Von Neumann Bottleneck – Cannot run processor while the memory is being accessed Event Driven Power usageUses a lot of energy to overcome bottleneck Runs cool(shown below)Runs hot(shown below) Recognize and complete complex patternsProcesses quantitative information efficiently