Modeling Compute In Memory With Biological Efficiency

The growing popularity of generative AI, which uses natural language to help users make sense of unstructured data, is forcing sweeping changes in how compute resources are designed and deployed. In a panel discussion on artificial intelligence at last week’s IEEE Electron Device Meeting, IBM’s Nicole Saulnier described it as a major breakthrough that should allow AI tools to assist huma... » read more

ReRAM Seeks To Replace NOR

Resistive RAM is gaining renewed attention as demand for faster and cheaper non-volatile memory alternatives continues to grow, particularly in applications such as automotive. Embedded flash has long left designers wishing for better write speeds and lower energy consumption, but as the leading edge of that technology shrunk to 28nm, another problem arose. Manufacturing flash memory at thos... » read more

Journey From Cell-Aware To Device-Aware Testing Begins

Early results of using device-aware testing on alternative memories show expanded test coverage, but this is just the start. Once the semiconductor industry realized that it was suffering from device failures even when test programs achieved 100% fault coverage, it went about addressing this disconnect between the way defects manifest themselves inside devices and the commonly used fault mod... » read more

In-Memory Computing: Assessing Multilevel RRAM-Based VMM Operations

A new technical paper titled "Experimental Assessment of Multilevel RRAM-Based Vector-Matrix Multiplication Operations for In-Memory Computing" was published by researchers at IHP (the Leibniz Institute for High Performance Microelectronics). Abstract: "Resistive random access memory (RRAM)-based hardware accelerators are playing an important role in the implementation of in-memory computin... » read more

Redesigning Core and Cache Hierarchy For A General-Purpose Monolithic 3D System

A technical paper titled "RevaMp3D: Architecting the Processor Core and Cache Hierarchy for Systems with Monolithically-Integrated Logic and Memory" was published by researchers at ETH Zürich, KMUTNB, NTUA, and University of Toronto. Abstract: "Recent nano-technological advances enable the Monolithic 3D (M3D) integration of multiple memory and logic layers in a single chip with fine-graine... » read more

Adaptive Memristive Hardware

A new technical paper titled "Self-organization of an inhomogeneous memristive hardware for sequence learning" was just published by researchers at University of Zurich, ETH Zurich, Université Grenoble Alpes, CEA, Leti and Toshiba. "We design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorp... » read more

Efficient Neuromorphic AI Chip: “NeuroRRAM”

New technical paper titled "A compute-in-memory chip based on resistive random-access memory" was published by a team of international researchers at Stanford, UCSD, University of Pittsburgh, University of Notre Dame and Tsinghua University. The paper's abstract states "by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present ... » read more

Technical Paper Round-Up: July 5

New technical papers added to Semiconductor Engineering’s library this week. [table id=36 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit for... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs

New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

End-to-End System for Object Localization By Coupling pMUTs to a Neuromorphic RRAM-based Computational Map

New research paper titled "Neuromorphic object localization using resistive memories and ultrasonic transducers" from researchers at CEA, LETI, Université Grenoble Alpes and others. Abstract "Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary... » read more

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