Accelerating deep neural network in-situ training with non-volatile and volatile memory based hybrid precision synapses
… trend of hybrid precision synapse is projected towards … calculation during error
backpropagation, the weight for convo… first stored into on-chip buffer or off-chip DRAM if on-chip …
backpropagation, the weight for convo… first stored into on-chip buffer or off-chip DRAM if on-chip …
Memristors—From in‐memory computing, deep learning acceleration, and spiking neural networks to the future of neuromorphic and bio‐inspired computing
… the binary and analog non-volatile storage capability is matrix-… network weight updates are
implemented on-chip in an event-… that have been explored toward realizing memristive-based …
implemented on-chip in an event-… that have been explored toward realizing memristive-based …
Compute-in-memory chips for deep learning: Recent trends and prospects
… For basics of emerging non-volatile memory (eNVM) devices and … So far, we have assumed
the on-chip memory capacity is … The DNN model size will be even larger towards GB-level for …
the on-chip memory capacity is … The DNN model size will be even larger towards GB-level for …
On-chip training spiking neural networks using approximated backpropagation with analog synaptic devices
… We design the SNN system by using the proposed on-chip training scheme with the GSDs,
which … on-chip training scheme that efficiently approximates the backpropagation algorithm …
which … on-chip training scheme that efficiently approximates the backpropagation algorithm …
Circuits and architectures for in-memory computing-based machine learning accelerators
… in both CMOS and emerging nonvolatile memory (NVM) … to overcome the memory
bottlenecks (both on-chip and off-… will be instrumental toward improving inmemory architectures. …
bottlenecks (both on-chip and off-… will be instrumental toward improving inmemory architectures. …
CoMN: Algorithm-Hardware Co-Design Platform for Non-Volatile Memory Based Convolutional Neural Network Accelerators
… and huge design space of CIM-based CNN acceleration system make cross-level co-design …
time and energy consumption of on-chip training[30]. Different from volatile-based CIM …
time and energy consumption of on-chip training[30]. Different from volatile-based CIM …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
… memory computing (AIMC) using non-volatile memory (NVM) elements is a promising
mixed-signal approach for DNN acceleration 13,14,15 , with weights stored using … , with on-chip or …
mixed-signal approach for DNN acceleration 13,14,15 , with weights stored using … , with on-chip or …
AILC: Accelerate on-chip incremental learning with compute-in-memory technology
… volatile memory (eNVMs) technologies, which include resistive random access memory (RRAM)
[8], phase change memory … 2.2 CIM-Based DNN Acceleration CIM paradigm accelerates …
[8], phase change memory … 2.2 CIM-Based DNN Acceleration CIM paradigm accelerates …
Neuro-inspired computing chips
… chips based on non-volatile memory. We also provide a future … must be mapped to on-chip
synaptic memories. As the weight … 77 or sign-based backpropagation 78 , are more suitable …
synaptic memories. As the weight … 77 or sign-based backpropagation 78 , are more suitable …
[HTML][HTML] A compute-in-memory chip based on resistive random-access memory
… Besides inference, the error back-propagation during gradient-… Network-on-chip and
program scheduling need to be … As resistive memory continues to scale towards offering tera-…
program scheduling need to be … As resistive memory continues to scale towards offering tera-…