John H. Naegle

A novel digital neuromorphic architecture efficiently facilitating complex synaptic response functions applied to liquid state machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU — demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.

Building networks for the wide and local areas using asynchronous transfer mode switches and synchronous optical network technology

Sandia National Laboratories is using a set of evolving technologies to develop a standards-based approach to wide- and local-area networking, which offers the potential of gigabit speeds. In particular, asynchronous transfer mode (ATM) switches and synchronous optical network (SONET) technologies were used to build a supercomputing network between its California and New Mexico sites and now is being deployed in the local-area environment. The progress of these endeavors and the lessons learned are discussed. >

Neuromorphic data microscope

In 2016, Lewis Rhodes Labs, (LRL), shipped the first commercially viable Neuromorphic Processing Unit, (NPU), branded as a Neuromorphic Data Microscope (NDM). This product leverages architectural mechanisms derived from the sensory cortex of the human brain to efficiently implement pattern matching. LRL and Sandia National Labs have optimized this product for streaming analytics, and demonstrated a 1,000x power per operation reduction in an FPGA format. When reduced to an ASIC, the efficiency will improve to 1,000,000x. Additionally, the neuromorphic nature of the device gives it powerful computational attributes that are counterintuitive to those schooled in traditional von Neumann architectures. The Neuromorphic Data Microscope is the first of a broad class of brain-inspired, time domain processors that will profoundly alter the functionality and economics of data processing.