IEEE Circuits and Systems Magazine - Q1 2021 - 32
functions, such as the sigmoid or the Rectified Linear
Unit (ReLU) in (42), are applied to obtain the values of the
feature maps. The pooling layer maps the features into a
lower resolution space, for example, with the function
in (43). Fully connected neural networks compute over
reduced spaces, applying weights to the inputs of artificial neurons that " fire " based on nonlinear functions.
The large amount of computation and the high memory
requirements are challenges faced in the development of
efficient CNNs, especially in devices with autonomy and
resource constraints. It will be shown how RNS, LNS, associated with fixed-point and FP representations, and SC
can be used to face these challenges.
conv " / w i y i + b
(41)
i
act " h t = tanh y ; h s =
1 ; h = max ^0, y h
RLU
1 + e -y
pool " p max = max ( y); p L2 =
/ y 2i
(42)
(43)
Although several proposals have been made to apply RNS to compute a CNN, only a few can be fully computed in the RNS domain [121]-[123]. These complete
solutions mitigate the overhead imposed by the nonlinear functions of the activation and pooling layers
by carefully selecting the RNS moduli sets. In [121], the
Microwave Resonator
Addresses and Couples
Qubits on Processor
Quantum Chip
Transmon Qubit:
Behaves Like an
Artificial Atom
Niobium Capacitor
100 µm
Josephson Junction:
Super Conducting
Inductor, Kept at
0.015 Kelvin
Figure 23. The transmon qubit and the quantum chip [169]: a type of superconducting charge qubit; the superconductors are
capacitatively shunted in order to decrease the sensitivity to charge noise.
32
IEEE CIRCUITS AND SYSTEMS MAGAZINE
FIRST QUARTER 2021
IEEE Circuits and Systems Magazine - Q1 2021
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