RAELLA: Reforming the arithmetic for efficient, low-resolution, and low-loss analog PIM: No retraining required!
Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …
Network (DNN) inference by reducing costly data movement and by using resistive RAM …
Inca: Input-stationary dataflow at outside-the-box thinking about deep learning accelerators
This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …
Era-bs: Boosting the efficiency of reram-based pim accelerator with fine-grained bit-level sparsity
Resistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural
network accelerators, thanks to its in-memory and in-situ analog computing abilities for …
network accelerators, thanks to its in-memory and in-situ analog computing abilities for …
Subgraph stationary hardware-software inference co-design
A growing number of applications depend on Machine Learning (ML) functionality and
benefits from both higher quality ML predictions and better timeliness (latency) at the same …
benefits from both higher quality ML predictions and better timeliness (latency) at the same …
Resistive neural hardware accelerators
K Smagulova, ME Fouda, F Kurdahi… - Proceedings of the …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that
real-world data can be learned, and decisions can be made in real time. However, their wide …
real-world data can be learned, and decisions can be made in real time. However, their wide …
Energy-efficient reram-based ml training via mixed pruning and reconfigurable adc
Machine learning (ML) models have gained prominence in solving real-world tasks.
However, implementing ML models is both compute-and memory-intensive. Domain-specific …
However, implementing ML models is both compute-and memory-intensive. Domain-specific …
RACE-IT: A reconfigurable analog CAM-crossbar engine for in-memory transformer acceleration
Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel
in a wide range of machine learning tasks. However, processing these models demands …
in a wide range of machine learning tasks. However, processing these models demands …
3A-ReRAM: Adaptive Activation Accumulation in ReRAM-Based CNN Accelerator
Z Zhang, J Jiang, Q Wang, Z Mao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
ReRAM-based computing is good at accelerating convolutional neural network (CNN)
inference due to its high computing parallelism, but its rigid crossbar structure may become …
inference due to its high computing parallelism, but its rigid crossbar structure may become …
[HTML][HTML] PRAP-PIM: A weight pattern reusing aware pruning method for ReRAM-based PIM DNN accelerators
Abstract Resistive Random-Access Memory (ReRAM) based Processing-in-Memory (PIM)
frameworks are proposed to accelerate the working process of DNN models by eliminating …
frameworks are proposed to accelerate the working process of DNN models by eliminating …
Redy: A novel reram-centric dynamic quantization approach for energy-efficient cnn inference
The primary operation in DNNs is the dot product of quantized input activations and weights.
Prior works have proposed the design of memory-centric architectures based on the …
Prior works have proposed the design of memory-centric architectures based on the …