The slides from the presentation are now available.
NEWM-202A-1: RRAM (New Memory Technologies Track) Wednesday 3:20
Paper Title: Making SiOx ReRAM a Cost-effective Embedded MemoryPaper Abstract: Among emerging NVM technologies, ReRAM’s process simplicity, low power consumption, and fast switching make it a compelling candidate for embedded NVM (eNVM) in low-power, always-connected devices. Commercializing ReRAM designs requires the ability to understand and control important factors affecting ReRAM performance and reliability. These include switching layer composition, density, and geometrical dimensions.
AIML-301-1:Using AI/ML for Flash Performance Scaling, Part 1 (AI/Machine Learning Track) Thursday 8:30
Paper Title: ReRAM for Implementing Neural Network SynapsesPaper Abstract: ReRAM technology is widely considered the best candidate for implementing artificial synapses for neuromorphic computing. The movement of ionic species in ReRAM cells closely resembles mechanisms typical of biological synapses in the human brain. Furthermore, neuromorphic circuits demonstrate good resilience to the intrinsic device variability and noise typical of ReRAM. A CEA/Leti test vehicle uses SiOx resistive memories from Weebit in a neuromorphic architecture to demonstrate a spiking neural network (SNN) composed of analog neurons and resistive synapses. The chosen topology is a fully-connected single-layer SNN, which can handle up to 144-dimensional input vectors and 10 output classes. The designed-integrate and fires neurons allow the use of existing supervised offline learning tools. Digit recognition using this SNN on the MNIST database will be demonstrated.
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(20min delay)