  Shifting computing architectures from von Neumann to event-based spiking
neural networks (SNNs) uncovers new opportunities for low-power processing of
sensory data in applications such as vision or sensorimotor control. Exploring
roads toward cognitive SNNs requires the design of compact, low-power and
versatile experimentation platforms with the key requirement of online learning
in order to adapt and learn new features in uncontrolled environments. However,
embedding online learning in SNNs is currently hindered by high incurred
complexity and area overheads. In this work, we present ODIN, a 0.086-mm$^2$
64k-synapse 256-neuron online-learning digital spiking neuromorphic processor
in 28nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of
12.7pJ. It leverages an efficient implementation of the spike-driven synaptic
plasticity (SDSP) learning rule for high-density embedded online learning with
only 0.68$\mu$m$^2$ per 4-bit synapse. Neurons can be independently configured
as a standard leaky integrate-and-fire (LIF) model or as a custom
phenomenological model that emulates the 20 Izhikevich behaviors found in
biological spiking neurons. Using a single presentation of 6k 16$\times$16
MNIST training images to a single-layer fully-connected 10-neuron network with
on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%
while consuming only 15nJ/inference at 0.55V using rank order coding. ODIN thus
enables further developments toward cognitive neuromorphic devices for
low-power, adaptive and low-cost processing.
