Energy-efficient Computation-In-Memory Architecture using Emerging Technologies
Rajendra Bishnoi (TU Delft - Computer Engineering)
Sumit Diware (TU Delft - Computer Engineering)
Anteneh Gebregiorgis (TU Delft - Computer Engineering)
Simon Thomann (Technische Universität München)
Sara Mannaa (Lyon Institute of Nanotechnology)
Bastien Deveautour (Lyon Institute of Nanotechnology)
Cedric Marchand (Lyon Institute of Nanotechnology)
Alberto Bosio (Lyon Institute of Nanotechnology)
Damien Deleruyelle (Lyon Institute of Nanotechnology)
Ian O'Connor (Lyon Institute of Nanotechnology)
Hussam Amrouch (Technische Universität München)
Said Hamdioui (TU Delft - Computer Engineering)
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Abstract
Deep Learning (DL) has recently led to remark-able advancements, however, it faces severe computation related challenges. Existing Von-Neumann-based solutions are dealing with issues such as memory bandwidth limitations and energy inefficiency. Computation-In-Memory (CIM) has the potential to address this problem by integrating processing elements directly into the memory architecture, reducing data movement and enhancing the overall efficiency of the system. In this work, we propose CIM architecture using three distinct emerging technologies. Firstly, a CIM architecture utilizing Ferroelectric Field-Effect Transistors (FeFET) is shown and the resulting errors from the analog compute scheme are injected into the emerging algorithm of Hyperdimensional Computing. Subsequently, we explore Vertical Nanowire Field-Effect Transistors (VNWFETs) based CIM within a 3D computing architecture, demonstrating improved energy efficiency and reconfigurability for CIM. Additionally, we improve the accuracy of the Resistive Random Access Memories (RRAM) based CIM architecture using two mapping-based solutions. These three technologies exhibit non-volatile characteristics, and when integrated into the CIM architecture, they yield significant advantages, including enhanced energy efficiency, reliability, and accuracy in computing processes.