# 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. - **Patent:** US 11507804 - **Original title:** Device and method for calculating convolution in a convolutional neural network - **Owner:** Commissariat a lEnergie Atomique et aux Energies Alternatives CEA - **Granted:** 2022 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, semiconductors, consumer_electronics, telecommunications, automotive ## What it does 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. ## 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). ## 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. ## Real-world examples 1. Neuromorphic AI chips 2. Edge AI accelerators for IoT devices 3. Specialized processors for autonomous vehicles 4. Hardware for real-time sensor data analysis 5. AI chips in smartphones and smart cameras ## Why it matters 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. ## 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). **Full plain-English explainer:** https://patentbrief.org/patent/us/11507804/device-and-method-for-calculating-convolution-in-a-convolutional-neural-network **Original patent:** https://patents.google.com/patent/US11507804 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Efficiently Updating Connections in AI Brains](https://patentbrief.org/patent/us/9256823/apparatus-and-methods-for-efficient-updates-in-spiking-neuron-network) — This patent describes a method for updating the connections in artificial "spiking neuron networks" more efficiently by only making changes when needed, saving computational power. - [How a Chip Uses Memory to Speed Up AI Calculations](https://patentbrief.org/patent/us/11741188/hardware-accelerated-discretized-neural-network) — This patent describes a specialized computer chip that uses non-volatile memory and analog signals to quickly perform calculations for artificial intelligence, especially for neural networks that need to remember past information. - [How IBM Uses Resistive Memory Chips to Speed Up AI Training](https://patentbrief.org/patent/us/10740671/convolutional-neural-networks-using-resistive-processing-unit-array) — A method for running AI neural networks directly on specialized hardware chips that store data as electrical resistance, making them faster and more energy-efficient than standard processors. - [How Artificial Neurons Learn from Spikes and Internal States](https://patentbrief.org/patent/us/9256215/apparatus-and-methods-for-generalized-state-dependent-learning-in-spiking-neuron) — This patent describes a method for artificial neurons to learn by adjusting their connections based on a history of incoming electrical 'spikes' and the neuron's own internal state, aiming to improve its performance. - [How to Make AI Run Faster on Smaller Computer Chips](https://patentbrief.org/patent/us/10373050/tensor-processing-unit-tpu) — A method to shrink complex AI models by converting their high-precision math into simpler, faster formats that run efficiently on mobile devices.