Exploiting Variability in Resistive Memory Devices for Cognitive Systems

In literature, different approaches point to the use of different resistive memory (RRAM) device families such as PCM [1], OxRAM, CBRAM [2], and STT-MRAM [3] for synaptic emulation in dedicated neuromorphic hardware. Most of these works justify the use of RRAM devices in hybrid learning hardware on grounds of their inherent advantages, such as ultra-high density, high endurance, high retention, CMOS compatibility, possibility of 3D integration, and low power consumption [4]. However, with the advent of more complex learning and weight update algorithms (beyond-STDP kinds), for example the ones inspired from Machine Learning, the peripheral synaptic circuit overhead considerably increases. Thus, use of RRAM cannot be justified on the merits of device properties alone. A more application-oriented approach is needed to further strengthen the case of RRAM devices in such systems that exploit the device properties also for peripheral nonsynaptic and learning circuitry, beyond the usual synaptic application alone.In this chapter, we discuss two novel designs utilizing the inherent variability in resistive memory devices to successfully implement modified versions of Extreme Learning Machines and Restricted Boltzmann Machines in hardware.