Use of transistors as analog synapses - Tan, Daryl Chin Shih (Nanyang Technological University, 2021)
When the need for cost saving cognitive computing keeps increasing, the conventional von Neumann architecture approaches its limitations. Neuromorphic computing architecture is inspired by the brain and has been widely explored during the past three decades due to its energy efficiency and massive connectivity. A critical component of the brain-inspired architecture is the synapse, which transmit electrical signals between neurons. Many devices such as memristors and phase-change memory have been researched as possible candidates for analog synapses. In this report, a CMOS-only and manufacturing-ready device- the transistor, is researched. This paper presents a study of the transistor on how the analog characteristics of the transistor can be used for neuromorphic applications by showing how the transistor can be used to simulate an analog synapse. Next, an algorithm for unsupervised learning, the Winner-Takes-All (WTA) clustering, are researched using the transistors as analog synapses. A software simulation of the neuromorphic computer built using transistors as analog synapses will test on the feasibility of hardware implementation. System performance will be evaluated using experimentally obtain transistor characteristics.