101 research outputs found
Deep Liquid State Machines with Neural Plasticity and On-Device Learning
The Liquid State Machine (LSM) is a recurrent spiking neural network designed for efficient processing of spatio-temporal streams of information. LSMs have several inbuilt features such as robustness, fast training and inference speed, generalizability, continual learning (no catastrophic forgetting), and energy efficiency. These features make LSM’s an ideal network for deploying intelligence on-device.
In general, single LSMs are unable to solve complex real-world tasks. Recent literature has shown emergence of hierarchical architectures to support temporal information processing over different time scales. However, these approaches do not typically investigate the optimum topology for communication between layers in the hierarchical network, or assume prior knowledge about the target problem and are not generalizable.
In this thesis, a deep Liquid State Machine (deep-LSM) network architecture is proposed. The deep-LSM uses staggered reservoirs to process temporal information on multiple timescales. A key feature of this network is that neural plasticity and attention are embedded in the topology to bolster its performance for complex spatio-temporal tasks. An advantage of the deep-LSM is that it exploits the random projection native to the LSM as well as local plasticity mechanisms to optimize the data transfer between sequential layers. Both random projections and local plasticity mechanisms are ideal for on-device learning due to their low computational complexity and the absence of backpropagating error. The deep-LSM is deployed on a custom learning architecture with memristors to study the feasibility of on-device learning. The performance of the deep-LSM is demonstrated on speech recognition and seizure detection applications
Recommended from our members
Status of the National Ignition Facility project
The ultimate goal of worldwide research in inertial confinement fusion (ICF) is to develop fusion as an inexhaustible, economic, environmentally safe source of electric power. Following nearly thirty years of laboratory and underground fusion experiments, the next step toward this goal is to demonstrate ignition and propagating burn of fusion fuel in the laboratory. The National Ignition Facility(NIF) Project is being constructed at Lawrence Livermore National Laboratory (LLNL), for just this purpose. NIF will use advanced Nd-glass laser technology to deliver 1.8 MJ of 0.35-um laser light in a shaped pulse, several nanoseconds in duration, achieving a peak power of 500 TW. A national community of U.S. laboratories is participating in this project, now in its final design phase. Franceand the United Kingdom are collaborating on development of required technology under bilateral agreements with the US. This paper presents thestatus of the laser design and development of its principal components and optical elements
- …