Lifeboat Foundation

A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.

The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.

According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”

Continue reading “One Step Closer to Deep Learning on Neuromorphic Hardware” »

Neuromorphic systems carry out robust and efficient neural computation using hardware implementations that operate in physical time. Typically they are event- or data-driven, they employ low-power, massively parallel hybrid analog/digital VLSI circuits, and they operate using the same physics of computation used by the nervous system. Although there are several forums for presenting research achievements in neuromorphic engineering, none are exclusively dedicated to this increasingly large research community. Either because they are dedicated to single disciplines, such as electrical engineering or computer science, or because they serve research communities which focus on analogous areas (such as biomedical engineering or computational neuroscience), but with fundamentally different goals and objectives. The mission of Neuromorphic Engineering is to provide a publication medium dedicated exclusively and specifically to this field. Topics covered by this publication include:  Analog and hybrid analog/digital electronic circuits for implementing neural processes, such as conductances, neurons, synapses, plasticity mechanisms, photoreceptors, cochleae, etc.  Neuromorphic circuits and systems for implementing real-time event-based neural processing architectures.  Hardware models of neural and sensorimotor processing systems, such as selective attention systems, coordinate transformation systems, auditory and/or visual processing systems, sensory fusion systems, etc.  Implementations of neural computational systems found in insects, birds, mammals, etc.  Embedded neuromorphic systems, including actuated or robotic platforms which process sensory signals and interact with the environment using event-based sensors and circuits. To ensure high quality and state-of-the-art material, publications should demonstrate experimental results, using physical implementations of neuromorphic systems, and possibly show the links between the artificial system and the neural/biological one they model.

Discover how and why neuromorphic and probabilistic computing solutions represent the next wave of AI capabilities.

Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.

A new photonic chip design drastically reduces energy needed to compute with light, with simulations suggesting it could run optical neural networks 10 million times more efficiently than its electrical counterparts.

Image: courtesy of the researchers, edited by MIT News.

In 2017, an online disinformation campaign spread against the “White Helmets,” claiming that the group of aid volunteers was serving as an arm of Western governments to sow unrest in Syria.

A new photonic chip could run optical neural networks 10 million times more efficiently than conventional chips.

The classical physical limit for computing energy is the Landauer limit that sets a lower bound to the minimum heat dissipated per bit erasing operation. Performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully connected and convolutional networks.

Previous photonic chips had bulky optical components that limited their use to relatively small neural networks. MIT researchers have a new photonic accelerator that uses more compact optical components and optical signal-processing techniques, to drastically reduce both power consumption and chip area. That allows the chip to scale to neural networks several orders of magnitude larger than its counterparts.

Continue reading “New Photonic Chip Will Push to Limits of Computational Energy Efficiency Ten Million Times Beyond Conventional Chips” »

AI chatbots are finally getting good — or, at the very least, they’re getting entertaining.

Case in point is r/SubSimulatorGPT2, an enigmatically-named subreddit with a unique composition: it’s populated entirely by AI chatbots that personify other subreddits. (For the uninitiated, a subreddit is a community on Reddit usually dedicated to a specific topic.)

How does it work? Well, in order to create a chatbot you start by feeding it training data. Usually this data is scraped from a variety of sources; everything from newspaper articles, to books, to movie scripts. But on r/SubSimulatorGPT2, each bot has been trained on text collected from specific subreddits, meaning that the conversations they generate reflect the thoughts, desires, and inane chatter of different groups on Reddit.

Continue reading “There’s a subreddit populated entirely by AI personifications of other subreddits” »

The drone is adapted from a robot that killed off coral’s predators.