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GB2620172B - Identifying one or more quantisation parameters for quantising values to be processed by a neural network - Google Patents

Identifying one or more quantisation parameters for quantising values to be processed by a neural network Download PDF

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Publication number
GB2620172B
GB2620172B GB2209612.7A GB202209612A GB2620172B GB 2620172 B GB2620172 B GB 2620172B GB 202209612 A GB202209612 A GB 202209612A GB 2620172 B GB2620172 B GB 2620172B
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Prior art keywords
quantising
identifying
processed
values
neural network
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GB2209612.7A
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GB2620172A (en
GB202209612D0 (en
Inventor
Csefalvay Szabolcs
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Imagination Technologies Ltd
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Imagination Technologies Ltd
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Application filed by Imagination Technologies Ltd filed Critical Imagination Technologies Ltd
Priority to GB2402394.7A priority Critical patent/GB2624564B/en
Priority to GB2209612.7A priority patent/GB2620172B/en
Publication of GB202209612D0 publication Critical patent/GB202209612D0/en
Priority to US18/216,383 priority patent/US20240135153A1/en
Priority to EP23182504.3A priority patent/EP4303770A1/en
Priority to EP23182503.5A priority patent/EP4310730A1/en
Priority to US18/216,461 priority patent/US20240143985A1/en
Publication of GB2620172A publication Critical patent/GB2620172A/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Neurology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Error Detection And Correction (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
GB2209612.7A 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network Active GB2620172B (en)

Priority Applications (6)

Application Number Priority Date Filing Date Title
GB2402394.7A GB2624564B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network
GB2209612.7A GB2620172B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network
EP23182503.5A EP4310730A1 (en) 2022-06-30 2023-06-29 Processing data using a neural network "nn" implemented in hardware
EP23182504.3A EP4303770A1 (en) 2022-06-30 2023-06-29 Identifying one or more quantisation parameters for quantising values to be processed by a neural network
US18/216,383 US20240135153A1 (en) 2022-06-30 2023-06-29 Processing data using a neural network implemented in hardware
US18/216,461 US20240143985A1 (en) 2022-06-30 2023-06-29 Identifying one or more quantisation parameters for quantising values to be processed by a neural network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GB2209612.7A GB2620172B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network

Publications (3)

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GB202209612D0 GB202209612D0 (en) 2022-08-17
GB2620172A GB2620172A (en) 2024-01-03
GB2620172B true GB2620172B (en) 2024-05-29

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GB2209612.7A Active GB2620172B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network
GB2402394.7A Active GB2624564B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network

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GB2402394.7A Active GB2624564B (en) 2022-06-30 2022-06-30 Identifying one or more quantisation parameters for quantising values to be processed by a neural network

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Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119312851B (en) * 2024-11-25 2025-10-24 四川大学 A low-bitwidth adaptive quantization method for convolutional neural networks for image classification

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170270408A1 (en) * 2016-03-16 2017-09-21 Hong Kong Applied Science and Technology Research Institute Company, Limited Method and System for Bit-Depth Reduction in Artificial Neural Networks
GB2580171A (en) * 2018-12-21 2020-07-15 Imagination Tech Ltd Methods and systems for selecting quantisation parameters for deep neural networks using back-propagation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170270408A1 (en) * 2016-03-16 2017-09-21 Hong Kong Applied Science and Technology Research Institute Company, Limited Method and System for Bit-Depth Reduction in Artificial Neural Networks
GB2580171A (en) * 2018-12-21 2020-07-15 Imagination Tech Ltd Methods and systems for selecting quantisation parameters for deep neural networks using back-propagation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHOI et al, December 2021, "DANCE: Differentiable Accelerator/Network Co-Exploration", ACM/IEEE Design Automation Conference, pp. 337-342, IEEE. *
UHLICH et al., 22 May 2020, "Mixed Precision DNNs: All You Need is a Good Parameterization", arxiv.org [online], available from https://arxiv.org/abs/1905.11452 [accessed 18 January 2023]. *
WANG et al, August 2020, "Differentiable Joint Pruning and Quantization for Hardware Efficiency", 16th European Conference on Computer Vision, Lecture Notes in Computer Science, Springer. *

Also Published As

Publication number Publication date
GB2624564B (en) 2025-01-01
GB2624564A (en) 2024-05-22
GB2620172A (en) 2024-01-03
GB202209612D0 (en) 2022-08-17

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