A Fully-Synthetized 20-Gate Digital Spike-Based Synapse with Embedded Online Learning

Frenkel, Charlotte [UCL] Indiveri, Giacomo [University of Zurich and ETH Zurich, Switzerland] Legat, Jean-Didier [UCL] Bol, David [UCL]

Neuromorphic engineering aims at building cognitive systems made of electronic neuron and synapse circuits. These emerging computing architectures have a high potential for real-world problems that are difficult to formalize and program, such as vision or sensorimotor control. In order to leverage the potential of neuromorphic engineering and study cognition principles in physical systems, the development of autonomous online learning is a key feature. However, to develop scalable systems that can be used in realistic applications, it is crucial to design compact and low-power hardware platforms. Here we analyze a spike-driven synaptic plasticity (SDSP) learning rule and show that it is particularly well suited for highly compact digital synapse implementations, especially if compared to conventional spike-timing-dependent plasticity (STDP) rules. Furthermore, we designed an asynchronous fully-synthesizable digital synapse circuit with embedded SDSP-based online learning features, and with programmability options for versatile computing. The proposed synapse implementation requires only 20 gates for a compact area of 25μm2 in a 28nm FDSOI CMOS process.