HERMES-Core—A 1.59-TOPS/mm2 PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs
We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …
Precision of bit slicing with in-memory computing based on analog phase-change memory crossbars
In-memory computing is a promising non-von Neumann approach to perform certain
computational tasks efficiently within memory devices by exploiting their physical attributes …
computational tasks efficiently within memory devices by exploiting their physical attributes …
ReAIM: A ReRAM-based Adaptive Ising Machine for Solving Combinatorial Optimization Problems
Recently, in light of the success of quantum computers, research teams have actively
developed quantum-inspired computers using classical computing technology. One notable …
developed quantum-inspired computers using classical computing technology. One notable …
Accelerating polynomial modular multiplication with crossbar-based compute-in-memory
Lattice-based cryptographic algorithms built on ring learning with error theory are gaining
importance due to their potential for providing post-quantum security. However, these …
importance due to their potential for providing post-quantum security. However, these …
Design-time Reference Current Generation for Robust Spintronic-based Neuromorphic Architecture
Neural Networks (NN) can be efficiently accelerated in a neuromorphic fabric based on
emerging resistive non-volatile memories (NVM), such as Spin Transfer Torque Magnetic …
emerging resistive non-volatile memories (NVM), such as Spin Transfer Torque Magnetic …
PRIVE: Efficient RRAM Programming with Chip Verification for RRAM-based In-Memory Computing Acceleration
As deep neural networks (DNNs) have been success-fully developed in many applications
with continuously increasing complexity, the number of weights in DNNs surges, leading to …
with continuously increasing complexity, the number of weights in DNNs surges, leading to …
Process and runtime variation robustness for spintronic-based neuromorphic fabric
Neural Networks (NN) can be efficiently accelerated using emerging resistive non-volatile
memories (eNVM), such as Spin Transfer Torque Magnetic RAM (STT-MRAM). However …
memories (eNVM), such as Spin Transfer Torque Magnetic RAM (STT-MRAM). However …
Fast and low-cost mitigation of ReRAM variability for deep learning applications
To overcome the programming variability (PV) of ReRAM crossbar arrays (RCAs), the most
common method is program-verify, which, however, has high energy and latency overhead …
common method is program-verify, which, however, has high energy and latency overhead …
Towards Reliable and Energy-Efficient RRAM Based Discrete Fourier Transform Accelerator
The Discrete Fourier Transform (DFT) holds a prominent place in the field of signal
processing. The development of DFT accelerators in edge devices requires high energy …
processing. The development of DFT accelerators in edge devices requires high energy …
RWriC: A Dynamic Writing Scheme for Variation Compensation for RRAM-based In-Memory Computing
RRAM-based compute-in-memory (CIM) suffers from programming variation issues,
specifically device-to-device variation (DDV) and cycle-to-cycle variation (CCV), which can …
specifically device-to-device variation (DDV) and cycle-to-cycle variation (CCV), which can …