Recent advances and future prospects for memristive materials, devices, and systems
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges
Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …
bottlenecked by data transfers between processors and memory. The emergence of data …
PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …
overcoming the fundamental energy efficiency limitations of digital logic. They have been …
Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
Implementing spiking neural networks on neuromorphic architectures: A review
Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …
systems to execute machine learning applications that are designed using Spiking Neural …
Transpimlib: Efficient transcendental functions for processing-in-memory systems
Processing-in-memory (PIM) promises to alleviate the data movement bottleneck in modern
computing systems. However, current real-world PIM systems have the inherent …
computing systems. However, current real-world PIM systems have the inherent …
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 …
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
OCC: An automated end-to-end machine learning optimizing compiler for computing-in-memory
Memristive devices promise an alternative approach toward non-Von Neumann
architectures, where specific computational tasks are performed within the memory devices …
architectures, where specific computational tasks are performed within the memory devices …