As a reward for our good behaviour I have just brought over from the NASA thread the following extracts from an SBIR software contract which Rocket had linked and challenged me to highlight anything of interest. Well the following is my take on what was interesting about this SBIR.
It is in my opinion pretty amazing stuff when you consider they mention Loihi and True North in the full knowledge that they cannot meet the specified requirements under COTS and SWaP. (PS: Somewhere I read something about Brainchip and open sourced hardware recently.) The link to the full document is at the end.
My opinion only DYOR
FF
AKIDA BALLISTA
DESCRIPTION: Conventional computing architectures are running up against a quantum limit in terms of transistor size and efficiency, sometimes referred to as the end of Moore’s Law. To regain our competitive edge, we need to find a way around this limit. This is especially relevant for small size, weight, and power (SWaP)-constrained platforms. For these systems, scaling Von Neumann computing becomes prohibitively expensive in terms of power and/or SWaP.Biologically inspired neural networks provide the basis for modern signal processing and classification algorithms.
Implementation of these algorithms on conventional computing hardware requires significant compromises in efficiency and latency due to fundamental design differences. A new class of hardware is emerging that more closely resembles the biological neuron model, also known as a spiking neuron model; mathematically describing the systems found in nature and may solve some of these limitations and bottlenecks. Recent work has demonstrated performance gains using these new hardware architectures and have shown equivalence to converge on a solution with the same accuracy [Ref 1].The most promising of the new class are based on Spiking Neural Networks (SNN) and analog Processing in Memory (PiM) where information is spatially and temporally encoded onto the network. It can be shown that a simple spiking network can reproduce the complex behavior found in the neural cortex with significant reduction in complexity and power requirements [Ref 2]……
“It is recommended to use open source languages, software, and hardware when possible.”…
PHASE III: Refine algorithms and test with hardware. Validate models with data provided by Naval Air Warfare Center (NAWC) Aircraft Division (AD)/Weapons Division (WD). Transition model to the warfare centers. Development of documentation, training manuals, and software maintenance may be required.Heavy commercial investments in machine learning and artificial intelligence will likely continue for the foreseeable future. Adoption of hardware that can deliver on orders of magnitude in SWaP performance for intelligent mobile machine applications is estimated to be worth 10^9-10^12 global dollars annually.) Provide the software tools needed to optimize the algorithms and hardware integration. This effort would be a significant contribution to this requirement. Industries that would benefit from successful technology development include automotive (self-driving vehicles), personal robots, and a variety of intelligent sensors.
KEYWORDS: Spiking Neural Network, Neuromorphic Computing, Modeling, Convolution Neural Network, Analog Memory, Processing in Memory
https://www.sbir.gov/node/1696107
Modeling Neuromorphic and Advanced Computing Architectures
OBJECTIVE:Develop a software tool to optimize the signal processing chain across various sensors and systems, e.g., radar, electronic warfare (EW), electro-optical/infrared (EO/IR), and communications, that consists of functional models that can be assembled to produce an integrated network model used to predict overall detection/classification, power, and throughput performance to make design trade-off decisions.
I'll just slip into this phone box and change into my MetaTF getup.