Thesis Proposal: Gaspar Tognetti
Title: Soroban: A Mixed-Signal Neuromorphic Processing in Memory Architecture
Abstract: To meet the scientific demand for future data-intensive processing for every day mundane tasks such as searching via images to the uttermost serious health care disease diagnosis in personalized medicine, we urgently need a new cloud computing paradigm and energy efficient i.e. “green” technologies. We believe that a brain-inspired approach that employs unconventional processing offers an alternative paradigm for BIGDATA computing.
My research aims to go beyond the state of the art processor in memory architectures. In the realm of un-conventional processors, charge based computing has been an attractive solution since it’s introduction with charged-coupled device (CCD) imagers in the seventies. Such architectures have been modified to compute-in-memory arrays that have been used for signal processing, neural networks and pattern recognition using the same underlying physics. Other work has utilized the same concept in the charge-injection devices (CIDs), which have also been used for similar pattern recognition tasks. However, these computing elements have not been integrated with the support infrastructure for high speed input/output commensurate with BIGDATA processing streaming applications. In this work, the CID concept is taken to a smaller CMOS 55nm node and has shown promising preliminary results as a multilevel input computing element for hardware inference applications. A mixed signal charge-based vector-vector multiplier (VMM) is explored which computes directly on a common readout line of a dynamic random-access memory (DRAM). Low power consumption and high area density is achieved by storing local parameters in a DRAM computing crossbar.