IEEE Circuits and Systems Magazine - Q2 2020 - 44
Table 3.
Summary of the strategies used in HD computing for accuracy and efficiency improvement.
Model Type**
Applications
Encode*
base/
level
train
test
PlatformT
AccuracyH
AccelerationF
Motivation
Application
QuantHD
[25]
1
B/B
B/T
B/T
F, C, G
Re, shuffle
Q, DR, F
speedup +
accuracy
speech, activity,
face, phone
position
VoiceHD
[28]
1
B/B
B
B
C
NN, Re
DR, B
Replace deep speech
learning
CompHD
[51]
3
P
N
N
F
N
DR, Comp
DR without
accuracy
loss
activity, gesture,
valve monitoring
AdaptHD
[54]
1
B/B
N
B
C
Re, N
B, Adapt
accuracy +
short time
Re
speech, face,
activity,
Cardiotocograms
BinHD [35]
1
B/B
B
B
C
Re
B
speedup
speech, face,
activity,
Cardiotocograms
SemiHD
[53]
1
B/B
B
B
F, C
Re, N
DR, B
Replace deep 17 popular
learning
datasets [63]
Language
[21]
2
B
B
B
F
\
B
energy
saving +
robustness
language
recognition
Character
[56]
3
B
B
B
\
Binary
DR
data
classification
in IoT
character
recognition
Laelap [43]
1
B
B
B
C, G
\
\
energy
efficiency
seizure detection
* three encoding methods. 1: record-based encoding, 2: N-gram-based encoding, 3: a novel method.
** symbol "/" is used in record-based encoding. B: binary, P: bipolar, T: Ternary, N: non-binary.
T
implementation platforms. F: FPGA, C: CPU, G: GPU.
H
strategies for accuracy improvement. Re: retraining, N: non-binary model, NN: neural network.
F
strategies for efficiency improvement. DR: dimension reduction, Q: quantization, B: binarization, F: FPGA, Comp: compression, Adapt: adaptive.
Table 4.
Partial list of applications based on HD computingF in [40].
Applications
Inputs (#)*
Classes (#)**
HD (%)
Baseline (%)
Language recognition [21, 30]
1
21
96.70%
97.90%
Text categorization [64]
1
8
94.20%
86.40%
Speech recognition [28]
1
26
95.30%
93.60%
EMG gesture recognition [23]
4
5
97.80%
89.70%
Flexible EMG gesture recognition [27]
64
5
96.60%
88.90%
EEG brain-machine interface [65]
64
2
74.50%
69.50%
ECoG seizure detection [66]
100
2
95.40%
94.30%
DNA sequencing [31]
1
\
99.74%
94.53%
Character recognition [56]
1
10
89.94%
\
* represents the number of input data.
** represents the number of class hypervectors to be trained and stored in the associative memory.
F
Other works, like [67, 68, 69, 70, 71, 72, 73, 74], are not listed in this table..
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