View source for Micron technology, inc. (20240232601). PERFORMING PROCESSING-IN-MEMORY OPERATIONS RELATED TO SPIKING EVENTS, AND RELATED METHODS, SYSTEMS AND DEVICES simplified abstract

=PERFORMING PROCESSING-IN-MEMORY OPERATIONS RELATED TO SPIKING EVENTS, AND RELATED METHODS, SYSTEMS AND DEVICES=


==Organization Name==

[[:Category:micron technology, inc.|micron technology, inc.]]


[[Category:micron technology, inc.]]

==Inventor(s)==

[[:Category:Dmitri Yudanov of Cordova CA (US)|Dmitri Yudanov of Cordova CA (US)]][[Category:Dmitri Yudanov of Cordova CA (US)]]

[[:Category:Sean S. Eilert of Penryn CA (US)|Sean S. Eilert of Penryn CA (US)]][[Category:Sean S. Eilert of Penryn CA (US)]]

[[:Category:Hernan A. Castro of Shingle Springs CA (US)|Hernan A. Castro of Shingle Springs CA (US)]][[Category:Hernan A. Castro of Shingle Springs CA (US)]]

[[:Category:Ameen D. Akel of Rancho Cordova CA (US)|Ameen D. Akel of Rancho Cordova CA (US)]][[Category:Ameen D. Akel of Rancho Cordova CA (US)]]

==PERFORMING PROCESSING-IN-MEMORY OPERATIONS RELATED TO SPIKING EVENTS, AND RELATED METHODS, SYSTEMS AND DEVICES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240232601 titled 'PERFORMING PROCESSING-IN-MEMORY OPERATIONS RELATED TO SPIKING EVENTS, AND RELATED METHODS, SYSTEMS AND DEVICES

==Simplified Explanation==
The patent application describes methods, apparatuses, and systems for processing spiking events in a spiking neural network using a memory system.

* Spiking events in a spiking neural network are processed through a memory system.
* The memory system stores destination neurons and passes through pre-synaptic spike events from source neurons at each time interval.
* The group of pre-synaptic spike events are subsequently stored in memory.

==Key Features and Innovation==
* Processing spiking events in a spiking neural network using a memory system.
* Storing destination neurons and pre-synaptic spike events in memory.
* Passing through pre-synaptic spike events from source neurons at each time interval.

==Potential Applications==
This technology could be applied in artificial intelligence, neuroscience research, and cognitive computing.

==Problems Solved==
This technology addresses the efficient processing of spiking events in a spiking neural network and the storage of pre-synaptic spike events.

==Benefits==
* Improved efficiency in processing spiking events.
* Enhanced memory storage capabilities for pre-synaptic spike events.

==Commercial Applications==
* Title: "Enhanced Spiking Neural Network Processing Technology"
* Potential commercial uses: AI applications, brain-computer interfaces, cognitive computing systems.
* Market implications: Increased efficiency in neural network processing, potential for advancements in AI technology.

==Prior Art==
Further research can be conducted in the field of spiking neural networks and memory systems to explore existing technologies and innovations.

==Frequently Updated Research==
Stay updated on advancements in spiking neural networks, memory systems, and artificial intelligence research for potential developments in this technology.

==Questions about Spiking Neural Network Processing Technology==
===1. How does this technology improve the efficiency of processing spiking events in a neural network?===
This technology enhances efficiency by utilizing a memory system to store and process spiking events in a spiking neural network, reducing computational load.

===2. What are the potential applications of this technology beyond neural networks?===
This technology could have applications in neuroscience research, cognitive computing, and artificial intelligence systems, expanding its potential impact beyond neural networks.


==Original Abstract Submitted==

methods, apparatuses, and systems for in- or near-memory processing are described. spiking events in a spiking neural network may be processed via a memory system. a memory system may store a group of destination neurons, and at each time interval in a series of time intervals of a spiking neural network (snn), pass through a group of pre-synaptic spike events from respective source neurons, wherein the group of pre-synaptic spike events are subsequently stored in memory.



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