Efficient Hardware for Spiking Neural Network Calculations
This patent describes a specialized processor designed to quickly calculate layers in a spiking convolutional neural network by using parallel computing units whose number doesn't depend on the network's size, making AI processing more efficient.
Original patent title: “Device and method for calculating convolution in a convolutional neural network”
This patent describes a specialized processor designed to quickly calculate layers in a spiking convolutional neural network by using parallel computing units whose number doesn't depend on the network's size, making AI processing more efficient. Granted to Commissariat a lEnergie Atomique et aux Energies Alternatives CEA in 2022 with 15 claims and 1 forward citation, and it is expected to expire in 2037.
Coverage
What does this patent actually cover?
This device computes at least one convolution layer within a spiking convolutional neural network when an input event occurs. It uses one or more 'convolution modules,' each containing 'elementary computing units' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). These units work in parallel to calculate the internal values of the network's neurons. A key feature is that the number of these elementary computing units is independent of the total number of neurons in the convolution layer (Claim 1). Each module maps the 'weight coefficients' (like filters) of the network's kernel to these computing units in parallel. The device also includes memory to store these computed neuron values and dynamically calculates where to store them based on the input event and layer parameters (Claim 1). For example, if a smart camera detects an object, this device processes the image data by having multiple small computing units simultaneously apply different filters to parts of the image, storing the intermediate results in dynamically assigned memory locations for further processing.
The gap
What does this patent NOT cover?
- Does not cover devices for non-spiking convolutional neural networks, as it specifically claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more → 'spiking convolutional neural network' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover architectures where the number of elementary computing units is directly dependent on the number of neurons in the convolution layer (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems that process convolution layers purely sequentially without parallel mapping of weight coefficients to computing units (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover devices that use fixed memory addresses for neuron internal values without dynamically computing them based on input events and layer parameters (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover neural network computations that do not involve convolution layers, such as purely recurrent neural networks or fully connected layers without a convolutional component (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is designing computing units that operate in parallel and whose count is independent of the neural network's size, combined with dynamic memory address calculation. This allows for flexible hardware utilization and efficient processing of sparse 'spiking' events, which is crucial for energy-efficient neuromorphic computing.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Neuromorphic AI chips
Edge AI accelerators for IoT devices
Specialized processors for autonomous vehicles
Hardware for real-time sensor data analysis
AI chips in smartphones and smart cameras
Why it matters
The bigger picture
Efficient computation for convolutional neural networks is crucial for modern AI applications, especially in areas like edge computing where power and speed are critical. This patent focuses on optimizing hardware for 'spiking' neural networks, which are known for their potential energy efficiency and ability to mimic biological brains more closely. By making these calculations faster and more adaptable, it helps advance the development of powerful yet low-power AI systems.
Filed
April 27, 2017
Granted
November 22, 2022
Market context
Who's building on this
Companies in this space
The Commissariat a l'Energie Atomique et aux Energies Alternatives (CEA), a French public research organization, is the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more → and continues to be active in advanced computing and AI research. Companies like Intel (with its Loihi chip), IBM (with NorthPole), and various startups in the neuromorphic computing space are actively developing hardware that aims for similar efficiency and parallel processing capabilities for AI workloads.
Market impact
This type of innovation contributes to the ongoing shift towards more energy-efficient and specialized AI hardware. By enabling faster and more flexible computation for spiking neural networks, it helps reduce the power consumption and latency of AI inference, particularly at the 'edge' (on devices rather than in the cloud). This can accelerate the adoption of AI in battery-powered devices, IoT, and real-time systems, potentially creating new product categories and expanding the reach of AI applications.
Claim 1 — Plain English
What this patent covers
This device computes at least one convolution layer within a spiking convolutional neural network when an input event occurs. It uses one or more 'convolution modules,' each containing 'elementary computing units' (Claim 1). These units work in parallel to calculate the internal values of the network's neurons. A key feature is that the number of these elementary computing units is independent of the total number of neurons in the convolution layer (Claim 1). Each module maps the 'weight coefficients' (like filters) of the network's kernel to these computing units in parallel. The device also includes memory to store these computed neuron values and dynamically calculates where to store them based on the input event and layer parameters (Claim 1). For example, if a smart camera detects an object, this device processes the image data by having multiple small computing units simultaneously apply different filters to parts of the image, storing the intermediate results in dynamically assigned memory locations for further processing.
The clever bit
The clever part is designing computing units that operate in parallel and whose count is independent of the neural network's size, combined with dynamic memory address calculation. This allows for flexible hardware utilization and efficient processing of sparse 'spiking' events, which is crucial for energy-efficient neuromorphic computing.
What it does not cover
- Does not cover devices for non-spiking convolutional neural networks, as it specifically claims 'spiking convolutional neural network' (Claim 1).
- Does not cover architectures where the number of elementary computing units is directly dependent on the number of neurons in the convolution layer (Claim 1).
- Does not cover systems that process convolution layers purely sequentially without parallel mapping of weight coefficients to computing units (Claim 1).
- Does not cover devices that use fixed memory addresses for neuron internal values without dynamically computing them based on input events and layer parameters (Claim 1).
- Does not cover neural network computations that do not involve convolution layers, such as purely recurrent neural networks or fully connected layers without a convolutional component (Claim 1).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
10/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$40K – $128K
Midpoint $80K · 10.7 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
15 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Bichler, O., LORRAIN, V., & Dupret, A. (2022). Efficient Hardware for Spiking Neural Network Calculations (U.S. Patent No. 11,507,804). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11507804/device-and-method-for-calculating-convolution-in-a-convolutional-neural-network
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Efficient Hardware for Spiking Neural Network Calculations cover?
This patent describes a specialized processor designed to quickly calculate layers in a spiking convolutional neural network by using parallel computing units whose number doesn't depend on the network's size, making AI processing more efficient.
Who owns patent US 11507804?
Commissariat a lEnergie Atomique et aux Energies Alternatives CEA owns this patent, granted in 2022.
When does this patent expire?
This patent is expected to expire on April 27, 2037, when the invention enters the public domain.
What is patent US 11507804 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
Efficient computation for convolutional neural networks is crucial for modern AI applications, especially in areas like edge computing where power and speed are critical. This patent focuses on optimizing hardware for 'spiking' neural networks, which are known for their potential energy efficiency and ability to mimic biological brains more closely. By making these calculations faster and more adaptable, it helps advance the development of powerful yet low-power AI systems.
What does this patent NOT cover?
Does not cover devices for non-spiking convolutional neural networks, as it specifically claims 'spiking convolutional neural network' (Claim 1).
Patent monitoring