Deconvolution Transformation
20
(1, 1) = A * e
(1, 3) = B * e
(3, 1) = C * e
(3, 3) = D * e
e
(1, 2) = A * d + B * f
(3, 2) = C * d + D * f
f
d
(1, 2) = A * d + B * f
(3, 2) = C * d + D * f
b
h
▸Compile a deconvolution layer into 4 convolution layers
I Original input feature map
e ofmap elements generated in this round are also stored
he buffer, and are too shaded.
terns. The key is to recognize that the four computation
erns are essentially four different convolutions, each con-
ving the original ifmap with a distinct kernel that is part
he original kernel. For instance, (2, 2), (2, 4), (4, 2), and
4) are generated by convolving
⇥
a c
g i
⇤
with ifmap. More
erally, the deconvolution in Fig. 6 can be calculated as:
b c
e f
h i
#
b
~ I = G ([e]~I,[d f]~I,
b
h
~I,
a c
g i
~I)
ere b
~ denotes deconvolution, ~ denotes standard convolu-
n, I denotes the ifmap, and G denotes the gather operation
t assembles the ofmap from the results of the four con-
utions. G can be simply implemented as a set of load
rations to the scratchpad memory (on-chip buffer).
Essentially, our algorithm decomposes the original 3⇥3
cient for convolutions.
also be extended to supp
which have more relaxe
We assume that the ac
(scratchpad memory) th
as output elements. The
hold all the data for a lay
in multiple rounds. Onl
loaded into the buffer ea
into the buffer in each ro
and is determined by th
The buffer is evenly s
buffer to support doub
computing the current ro
data needed for the next
The next round does no
This design choice guara
Deconvolution
ents generated in this round are also stored
d are too shaded.
y is to recognize that the four computation
ntially four different convolutions, each con-
nal ifmap with a distinct kernel that is part
ernel. For instance, (2, 2), (2, 4), (4, 2), and
ted by convolving
⇥
a c
g i
⇤
with ifmap. More
convolution in Fig. 6 can be calculated as:
= G ([e]~I,[d f]~I,
b
h
~I,
a c
g i
~I)
deconvolution, ~ denotes standard convolu-
e ifmap, and G denotes the gather operation
he ofmap from the results of the four con-
n be simply implemented as a set of load
cient for convolutions. Alte
also be extended to support
which have more relaxed co
We assume that the accele
(scratchpad memory) that h
as output elements. The bu
hold all the data for a layer. T
in multiple rounds. Only pa
loaded into the buffer each r
into the buffer in each round
and is determined by the lo
The buffer is evenly split
buffer to support double-b
computing the current round
data needed for the next rou
Convolution
h a 3⇥3 kernel split into four sub-kernels. With a
tiling strategy W = 2,H = 2,C1 = 1,C2 = 2,C3 =
only the shaded elements are loaded into the buffer.
p elements generated in this round are also stored
fer, and are too shaded.
The key is to recognize that the four computation
re essentially four different convolutions, each con-
he original ifmap with a distinct kernel that is part
ginal kernel. For instance, (2, 2), (2, 4), (4, 2), and
generated by convolving
⇥
a c
g i
⇤
with ifmap. More
, the deconvolution in Fig. 6 can be calculated as:
c
f
i
#
b
~ I = G ([e]~I,[d f]~I,
b
h
~I,
a c
g i
~I)
denotes deconvolution, ~ denotes standard convolu-
notes the ifmap, and G denotes the gather operation
mbles the ofmap from the results of the four con-
sists of a 2D systolic array, in whic
(PE) performs one MAC operation
arrays use a simple neighbor-to-
mechanism that simplifies the con
cient for convolutions. Alternativ
also be extended to support SIMD-
which have more relaxed control w
We assume that the accelerator h
(scratchpad memory) that holds ac
as output elements. The buffer siz
hold all the data for a layer. Therefo
in multiple rounds. Only part of th
loaded into the buffer each round. E
into the buffer in each round is criti
and is determined by the loop tilin
The buffer is evenly split into a w
buffer to support double-bufferin
computing the current round using
data needed for the next round is p
Gather (stores to scratchpad)
c
a
i
g
(2, 2) = A * a + B * c + C * g + D * i
( )
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