PATENT CLAIM ANALYSIS

Application Number: 16273031
Application Type: Utility
Filing Date: 2019-02
Publication Date: 2019-08
Patent Classification: ["706", "025000"]

Abstract:
The present disclosure provides an integrated circuit chip device and related product thereof. The integrated circuit chip device includes an external interface and a processing circuit. The processing circuit is configured to quantize the first layer input data and the first layer weight group data to obtain a first layer quantized input data and a first layer quantized weight group data; query a first layer output data corresponding to the first layer quantized input data and the first layer quantized weight group data from a preset output result table, determine the first layer output data as a second layer input data, and input the second layer input data into n-1 layers to execute forward operations to obtain nth layer output data; the n th  layer output data gradients is determined according to the n th  layer output data and the n th  layer back operations is obtained according to the training instructions.

Claim (Index 1):
An integrated circuit chip device for training a neural network that includes n layers and n being an integer greater than 1, comprising:\n an external interface configured to receive one or more training instructions; a processing circuit configured to:\n determine a first layer input data and a first layer weight group data, \n quantize the first layer input data and the first layer weight group data to obtain a first layer quantized input data and a first layer quantized weight group data, \n query a first layer output data corresponding to the first layer quantized input data and the first layer quantized weight group data from a preset output result table, \n determine the first layer output data as a second layer input data, and input the second layer input data into n-1 layers to execute forward operations to obtain nth layer output data, \n determine n th  layer output data gradients of the n th  layer output data, \n obtain n th  layer back operations among the back operations of n layers of the training instructions, \n quantize the n th  layer output data gradients to obtain n th  layer quantized output data gradients, \n query n th  layer input data gradients corresponding to the n th  layer quantized output data gradients and a n th  layer quantized input data from the preset output result table, \n query nth layer weight group gradients corresponding to the n th  layer quantized output data gradients and a n th  layer quantized weight group data from the preset output result table, \n update a weight group data of n layers of the n th  layer weight group gradients, \n determine the n th  input data gradients as (n-1) th  output data gradients, \n input the n th  input data gradients into n-1 layers to execute back operations to obtain n-1 weight group data gradients, and \n update n-1 weight group data corresponding to the n-1 weight group data gradients of the n-1 weight group data gradients, wherein the weight group data of each layer comprises at least two weights.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 99.0
- Lexical Diversity: 2.65
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['16075540', '16038083', '10571602', '15655203', '16228357']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3495041066358735
- 35 USC 102 Novelty (BERT): 0.4698278838441871
- Combined Prediction Score: 0.3615364843567049
- Mean Citation Score: 157.77427
- Max Citation Score: 163.24976
- Similarity Product: 119.2791487274742

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test