Description:AS ATTACHED , Claims:I/We Claim:
1. Data stream (45) having a representation of a neural network (10) encoded
thereinto, the data stream (45) comprising serialization parameter (102)
indicating a coding order (104) 5 at which neural network parameters (32),
which define neuron interconnections (22, 24) of the neural network (10),
are encoded into the data stream (45), wherein the serialization parameter
(102) is an n-ary parameter which indicates the coding order (104) out of a
set (108) of n coding orders (104).
10
2. Apparatus for encoding a representation of a neural network (10) into a data
stream (45), wherein the apparatus is configured to provide the data stream
(45) with a serialization parameter (102) indicating a coding order (104) at
which neural network parameters (32), which define neuron
15 interconnections (22, 24) of the neural network, are encoded into the data
stream (45), wherein the serialization parameter (102) is an n-ary parameter
which indicates the coding order (104) out of a set (108) of n coding orders
(104).
20 3. Apparatus for decoding a representation of a neural network (10) from a
data stream (45), wherein the apparatus is configured to decode from the
data stream (45) a serialization parameter (102) indicating a coding order
(104) at which neural network parameters (32), which define neuron
interconnections (22, 24) of the neural network, are encoded into the data
25 stream (45), wherein the serialization parameter (102) is an n-ary parameter
which indicates the coding order (104) out of a set (108) of n coding orders
(104).
4. Apparatus of claim 3, wherein the data stream is structured into one or more
30 individually accessible portions (200), each individually accessible portion
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representing a corresponding neural network layer (210, 30) of the neural
network, and
wherein the apparatus is configured to decode serially, from the data
stream (45), neural network parameters, which define neuron
interconnections (22, 24) 5 of the neural network within a predetermined
neural network layer, and
use the coding order (104) to assign neural network parameters serially
decoded from the data stream (45) to the neuron interconnections (22, 24).
10 5. Apparatus of claim 3 or claim 4, wherein the serialization parameter (102)
is indicative of a permutation using which the coding order (104) permutes
neurons (14, 18, 20) of a neural network layer (210, 30) relative to a default
order.
15 6. Apparatus of claim 5, wherein the permutation orders the neurons (14, 18,
20) of the neural network layer (210, 30) in a manner so that the neural
network parameters (32) monotonically increase along the coding order
(104) or monotonically decrease along the coding order (104).
20 7. Apparatus of claim 5, wherein the permutation orders the neurons (14, 18,
20) of the neural network layer (210, 30) in a manner so that, among
predetermined coding orders signalable by the serialization parameter (102),
a bitrate for coding the neural network parameters (32) into the data stream
(45) is lowest for the permutation indicated by the serialization parameter
25 (102).
8. Apparatus of any previous claim 3 to 7, wherein the neural network
parameters (32) comprise weights and biases.
30 9. Apparatus of any previous claim 3 to 8, wherein the apparatus is configured
to:
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decode, from the data stream, individually accessible sub-portions (43, 44,
240), into which individually accessible portions (200) the data stream is
structured, each sub-portion (43, 44, 240) representing a corresponding
neural network portion of the neural network, so that each sub-portion (43,
44, 240) is completely traversed 5 by the coding order (104) before a
subsequent sub-portion is traversed by the coding order (104).
10. Apparatus of any of claims 4 to 9, wherein the apparatus is configured to
decode, from the data stream, start codes (242) at which each individually
10 accessible portion (200) or sub-portion (43, 44, 240) begins, and/or pointers
(220, 244) pointing to beginnings of each individually accessible portion or
sub-portion, and/or pointers data stream lengths (246) of each individually
accessible portion or sub-portion for skipping the respective individually
accessible portion or sub-portion in parsing the data stream.
15
11. Apparatus of any of the previous claims 3 to 10, wherein the apparatus is
configured to decode, from the data stream, a numerical computation
representation parameter (120) indicating a numerical representation and bit
size at which the neural network parameters (32) are to be represented when
20 using the neural network (10) for inference.
12. Apparatus of any of the previous claims 3 to 11, wherein the data stream
(45), is structured into individually accessible sub-portions (43, 44, 240),
each individually accessible sub-portion representing a corresponding
25 neural network portion of the neural network, so that each individually
accessible sub-portion is completely traversed by the coding order (104)
before a subsequent individually accessible sub-portion is traversed by the
coding order (104), wherein the apparatus is configured to decode, from the
data stream (45), for a predetermined individually accessible sub-portion the
30 neural network parameter and a type parameter indicting a parameter type
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of the neural network parameter decoded from the predetermined
individually accessible sub-portion.
13. Apparatus of claim 12, wherein the type parameter discriminates, at least,
5 between neural network weights and neural network biases.
14. Apparatus of any of the previous claims 3 to 13, wherein the data stream
(45), is structured into one or more individually accessible portions (200),
each one or more individually accessible portion representing a
10 corresponding neural network layer (210, 30) of the neural network, and
wherein the apparatus is configured to decode, from the data stream
(45), for a predetermined neural network layer, a neural network layer type
parameter (130) indicating a neural network layer type of the predetermined
neural network layer of the neural network.
15
15. Apparatus of claim 14, wherein the neural network layer type parameter
(130) discriminates, at least, between a fully-connected and a convolutional
layer type.
20 16. Apparatus of any of claims 3 to 15, wherein the apparatus is configured to
decode a representation of a neural network (10) from the data stream (45),
wherein the data stream (45) is structured into one or more individually
accessible portions (200), each individually accessible portion representing
a corresponding neural network layer (210, 30) of the neural network, and
25 wherein the data stream (45) is, within a predetermined portion, further
structured into individually accessible sub-portions (43, 44, 240), each subportion
(43, 44, 240) representing a corresponding neural network portion
of the respective neural network layer (210, 30) of the neural network,
wherein the apparatus is configured to decode from the data stream (45), for
30 each of one or more predetermined individually accessible sub-portions (43,
44, 240)
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a start code (242) at which the respective predetermined individually
accessible sub-portion begins, and/or
a pointer (244) pointing to a beginning of the respective predetermined
individually accessible sub-portion, and/or
a data stream length parameter indicating 5 a data stream length (246) of the
respective predetermined individually accessible sub-portion for skipping
the respective predetermined individually accessible sub-portion in parsing
the data stream (45).
10 17. Apparatus of claim 16, wherein the apparatus is configured to decode, from
the data stream (45), the representation of the neural network using contextadaptive
arithmetic decoding and using context initialization at a start of
each individually accessible portion and each individually accessible subportion.
15
18. Apparatus of any previous claim 3 to 17, wherein the apparatus is configured
to decode a representation of a neural network (10) from a data stream (45),
wherein the data stream (45) is structured into individually accessible
portions (200), each portion representing a corresponding neural network
20 portion of the neural network, wherein the apparatus is configured to decode
from the data stream (45), for each of one or more predetermined
individually accessible portions, an identification parameter (310) for
identifying the respective predetermined individually accessible portion.
25 19. Apparatus of claim 18, wherein the identification parameter (310) is related
to the respective predetermined individually accessible portion via a hash
function or error detection code or error correction code.
20. Apparatus of claim 18 or claim 19, wherein the apparatus is configured to
30 decode, from the data stream (45), a higher-level identification parameter
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(310) for identifying a collection of more than one predetermined
individually accessible portion.
21. Apparatus of claim 20, wherein the higher-level identification parameter
(310) is related to the identification 5 parameters (310) of the more than one
predetermined individually accessible portion via a hash function or error
detection code or error correction code.
22. Apparatus of any previous claim 3 to 21, wherein the apparatus is configured
10 to decode a representation of a neural network (10) from a data stream (45),
wherein the data stream (45) is structured into individually accessible
portions (200), each portion representing a corresponding neural network
portion of the neural network, wherein the apparatus is configured to decode
from the data stream (45), for each of one or more predetermined
15 individually accessible portions a supplemental data (350) for
supplementing the representation of the neural network.
23. Apparatus of claim 22, wherein the data stream (45) indicates the
supplemental data (350) as being dispensable for inference based on the
20 neural network.
24. Apparatus of claim 22 or claim 23, wherein the apparatus is configured to
decode the supplemental data (350) for supplementing the representation of
the neural network for the one or more predetermined individually
25 accessible portions (200) from further individually accessible portions,
wherein the data stream (45) comprises for each of the one or more
predetermined individually accessible portions a corresponding further
predetermined individually accessible portion relating to the neural network
portion to which the respective predetermined individually accessible
30 portion corresponds.
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25. Apparatus of any previous claim 22 to 24, wherein the supplemental data
(350) relates to:
relevance scores of neural network parameters (32), and/or
perturbation ro 5 bustness of neural network parameters (32).
26. Apparatus of any previous claim 3 to 25, for decoding a representation of a
neural network (10) from a data stream (45), wherein the apparatus is
configured to decode from the data stream (45) hierarchical control data
10 (400) structured into a sequence (410) of control data portions (420),
wherein the control data portions provide information on the neural network
at increasing details along the sequence of control data portions.
27. Apparatus of claim 26, wherein at least some of the control data portions
15 (420) provide information on the neural network which is partially
redundant.
28. Apparatus of claim 26 or claim 27, wherein a first control data portion
provides the information on the neural network by way of indicating a
20 default neural network type implying default settings and a second control
data portion comprises a parameter to indicate each of the default settings.
29. Apparatus for performing an inference using a neural network, comprising:
25 an apparatus for decoding a data stream (45) according to any of claims 3 to
28, so as to derive from the data stream (45) the neural network, and
a processor configured to perform the inference based on the neural network.
30 30. Method for encoding a representation of a neural network into a data stream
(45), comprising providing the data stream with a serialization parameter
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indicating a coding order at which neural network parameters, which define
neuron interconnections of the neural network, are encoded into the data
stream, wherein the serialization parameter (102) is an n-ary parameter
which indicates the coding order (104) out of a set (108) of n coding orders
5 (104).
31. Method for decoding a representation of a neural network from a data
stream, comprising decoding from the data stream a serialization parameter
indicating a coding order at which neural network parameters, which define
10 neuron interconnections of the neural network, are encoded into the data
stream, wherein the serialization parameter (102) is an n-ary parameter
which indicates the coding order (104) out of a set (108) of n coding orders
(104).
15 32. Computer program for, when executed by a computer, causing the computer
to perform the method of claim 30 or claim 31.