Deep physical neural networks trained with backpropagation
Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …
their energy requirements now increasingly limit their scalability. Deep-learning …
[HTML][HTML] An analog-AI chip for energy-efficient speech recognition and transcription
Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
Deployment of artificial intelligence models on edge devices: A tutorial brief
Artificial intelligence (AI) on an edge device has enormous potential, including advanced
signal filtering, event detection, optimization in communications and data compression …
signal filtering, event detection, optimization in communications and data compression …
Fully on-chip MAC at 14 nm enabled by accurate row-wise programming of PCM-based weights and parallel vector-transport in duration-format
Hardware acceleration of deep learning using analog non-volatile memory (NVM) requires
large arrays with high device yield, high accuracy Multiply-ACcumulate (MAC) operations …
large arrays with high device yield, high accuracy Multiply-ACcumulate (MAC) operations …
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 …
Optimised weight programming for analogue memory-based deep neural networks
Analogue memory-based deep neural networks provide energy-efficiency and per-area
throughput gains relative to state-of-the-art digital counterparts such as graphics processing …
throughput gains relative to state-of-the-art digital counterparts such as graphics processing …
A heterogeneous and programmable compute-in-memory accelerator architecture for analog-ai using dense 2-d mesh
We introduce a highly heterogeneous and programmable compute-in-memory (CIM)
accelerator architecture for deep neural network (DNN) inference. This architecture …
accelerator architecture for deep neural network (DNN) inference. This architecture …
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference
ABSTRACT Analog In-Memory Computing (AIMC) is a promising approach to reduce the
latency and energy consumption of Deep Neural Network (DNN) inference and training …
latency and energy consumption of Deep Neural Network (DNN) inference and training …
Alpine: Analog in-memory acceleration with tight processor integration for deep learning
Analog in-memory computing (AIMC) cores offers significant performance and energy
benefits for neural network inference with respect to digital logic (eg, CPUs). AIMCs …
benefits for neural network inference with respect to digital logic (eg, CPUs). AIMCs …