18612881. ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS simplified abstract (GOOGLE LLC)
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 18612881 titled 'ACCELERATING NEURAL NETWORKS IN HARDWARE USING INTERCONNECTED CROSSBARS
The computing unit described in the patent application is designed to accelerate a neural network by utilizing interconnected analog crossbar circuits.
- Input unit consists of digital-to-analog and analog-to-digital conversion units to process signals.
- Interconnected analog crossbar circuits perform operations on analog signals based on matrix weights stored by crosspoints.
- The system converts analog signals into digital output vectors for neural network processing.
Potential Applications:
- Accelerating neural network computations in artificial intelligence systems.
- Improving efficiency and speed of deep learning algorithms.
- Enhancing performance of machine learning models in various applications.
Problems Solved:
- Addressing the need for faster processing of neural networks.
- Optimizing the utilization of analog signals in computational tasks.
- Increasing the overall efficiency of AI systems.
Benefits:
- Speeding up neural network training and inference processes.
- Reducing power consumption in AI hardware.
- Enhancing the accuracy and reliability of machine learning models.
Commercial Applications:
- AI hardware development for data centers and edge devices.
- Integration into autonomous vehicles for real-time decision-making.
- Implementation in healthcare systems for medical image analysis.
Questions about the technology: 1. How does the analog-to-digital conversion process improve neural network acceleration? 2. What are the key advantages of using interconnected analog crossbar circuits in AI computing?
Frequently Updated Research: Ongoing studies focus on optimizing the performance of interconnected analog crossbar circuits for even faster neural network acceleration.
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.
