چكيده به لاتين
Most of today's hardware is based on the Neumann architecture, which separates memory and processing. As we move forward, neuromorphic processing can transform everything in the technology industry, from programming to hardware. The human brain is weak in numerically calculating exact values, while it performs better in intelligent information processing tasks such as object recognition, sound and image perception of computers. As the name implies, neuromorphic processing works using a model inspired by the mechanism of the neural network mechanism of the human brain. Unlike supercomputers, which are the size of a full room, the human brain is very compact and requires much less energy than most supercomputers due to its parallelism. So low power consumption in these circuits is very important. In this thesis, while examining the types of neuromorphic circuits, one of the neuromorphic circuits called Mr.AlZahrani circuit, which has a special structure and characteristics of this circuit and factors for increasing power consumption in this type of circuits, which was very challenging, was thoroughly studied. Necessary measures should be taken to reduce it as much as possible.
To achieve this goal, we made changes to the baseline neuron circuit to reduce power consumption in the said circuit. First, we connected the first floor of the circuit to the VCC instead of ground, ie a voltage greater than "0" so as not to waste energy. On the last floor, we no longer connected the earth to the VCC, because we have both "0" and "1" current strongly. Instead, we used an NMos transistor between the pull-up network and the pull-down network to connect its gate to the drain. The use of this single transistor prevented the pull-up network and the pull-down network from being activated simultaneously, which in itself would significantly reduce power consumption.
According to the results of the implementation of the proposed method, in 180nm technology, the power consumption of 694 microwatts of base neurons and 605 microwatts of recommended neurons and also in 350nm technology, the power consumption of 1533 microwatts of basic neurons and 1507 microwatts of recommended neurons are obtained, which reduces the power of this circuit by about 14% in one neuron.
The method presented in this thesis can be used in neuromorphic circuits to reduce and minimize power consumption. PSPICE software was used to simulate this circuit.
