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Found 6 papers, 1 papers with code
Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units
To leverage the strengths of TPUs for convolutional layers and IMAC circuits for dense layers, we propose a unified learning algorithm that incorporates mixed-precision training techniques to mitigate potential accuracy drops when deploying models on the TPU-IMAC architecture.
IMAC-Sim: A Circuit-level Simulator For In-Memory Analog Computing Architectures
Thus, in this paper, we develop IMAC-Sim, a circuit-level simulator for the design space exploration of IMAC architectures.
Detecting Conspiracy Theory Against COVID-19 Vaccines
Since the beginning of the vaccination trial, social media has been flooded with anti-vaccination comments and conspiracy beliefs.
Reliability-Aware Deployment of DNNs on In-Memory Analog Computing Architectures
Conventional in-memory computing (IMC) architectures consist of analog memristive crossbars to accelerate matrix-vector multiplication (MVM), and digital functional units to realize nonlinear vector (NLV) operations in deep neural networks (DNNs).
MRAM-based Analog Sigmoid Function for In-memory Computing
We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter.
Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architectures
Fully-analog in-memory computing (IMC) architectures that implement both matrix-vector multiplication and non-linear vector operations within the same memory array have shown promising performance benefits over conventional IMC systems due to the removal of energy-hungry signal conversion units.
