Google llc (20240232603). ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS simplified abstract
Contents
ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS
Organization Name
Inventor(s)
Pierre-Luc Cantin of Palo Alto CA (US)
ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232603 titled 'ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS
The abstract describes a computing unit designed to accelerate a neural network by utilizing interconnected analog crossbar circuits.
- Input unit includes digital-to-analog and analog-to-digital conversion units.
- Analog crossbar circuits perform operations on analog signals based on matrix weights stored by crosspoints.
- Interconnected circuits enable signal processing and conversion within the neural network.
Potential Applications: - Accelerating neural network training and inference processes. - Improving efficiency and speed of artificial intelligence applications. - Enhancing performance of machine learning algorithms in various industries.
Problems Solved: - Addressing the need for faster and more efficient neural network computations. - Overcoming limitations of traditional digital computing units in handling complex AI tasks.
Benefits: - Increased speed and efficiency in neural network operations. - Enhanced performance and accuracy of AI systems. - Potential for advancements in deep learning and artificial intelligence research.
Commercial Applications: Title: "Neural Network Acceleration Computing Unit for AI Applications" This technology can be utilized in industries such as healthcare, finance, autonomous vehicles, and cybersecurity for faster and more accurate AI processing.
Prior Art: Readers can explore existing patents related to neural network acceleration, analog computing units, and crossbar circuits for further research.
Frequently Updated Research: Stay updated on advancements in analog computing, neural network acceleration, and AI hardware development for potential improvements in this technology.
Questions about Neural Network Acceleration Computing Unit: 1. How does this computing unit compare to traditional digital accelerators in terms of performance? 2. What are the potential scalability challenges of implementing interconnected analog crossbar circuits in large-scale neural networks?
Original Abstract Submitted
a computing unit for accelerating a neural network is disclosed. the computing unit include an input unit that includes a digital-to-analog conversion unit and an analog-to-digital conversion unit that is configured to receive an analog signal from the output of a last interconnected analog crossbar circuit of a plurality of analog crossbar circuits and convert the second analog signal into a digital output vector, and a plurality of interconnected analog crossbar circuits that include the first interconnected analog crossbar circuit and the last interconnected crossbar circuits, wherein a second interconnected analog crossbar circuit of the plurality of interconnected analog crossbar circuits is configured to receive a third analog signal from another interconnected analog crossbar circuit of the plurality of interconnected crossbar circuits and perform one or more operations on the third analog signal based on the matrix weights stored by the crosspoints of the second interconnected analog crossbar.
