[HTML][HTML] Prospects and challenges of electrochemical random-access memory for deep-learning accelerators
The ever-expanding capabilities of machine learning are powered by exponentially growing
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing
SX Go, KG Lim, TH Lee, DK Loke - Small Science, 2024 - Wiley Online Library
Separate memory and processing units are utilized in conventional von Neumann
computational architectures. However, regarding the energy and the time, it is costly to …
computational architectures. However, regarding the energy and the time, it is costly to …
Organic memristor with synaptic plasticity for neuromorphic computing applications
J Zeng, X Chen, S Liu, Q Chen, G Liu - Nanomaterials, 2023 - mdpi.com
Memristors have been considered to be more efficient than traditional Complementary Metal
Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are …
Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are …
Improving Efficiency in Multi-modal Autonomous Embedded Systems through Adaptive Gating
The parallel advancement of AI and IoT technologies has recently boosted the development
of multi-modal computing (M 2 C) on pervasive autonomous embedded systems (AES). M 2 …
of multi-modal computing (M 2 C) on pervasive autonomous embedded systems (AES). M 2 …
Edge-side fine-grained sparse CNN accelerator with efficient dynamic pruning scheme
With the rapid development of the Internet of Things (IoT), it has become a common concern
of academia and industry to provide real-time high performance services for edge-side …
of academia and industry to provide real-time high performance services for edge-side …
Victor: A variation-resilient approach using cell-clustered charge-domain computing for high-density high-throughput MLC CiM
Multi-level cell (MLC) NVM-based CiM has become a promising candidate in computing-in-
memory (CiM) designs because of its non-volatility, high cell density, and improving …
memory (CiM) designs because of its non-volatility, high cell density, and improving …
A heterogeneous microprocessor for intermittent AI inference using nonvolatile-SRAM-based compute-in-memory
Compute-In-Memory (CIM), characterized by efficient matrix-vector multiplication, has been
recognized as a promising candidate technology for edge AI computing. However, applying …
recognized as a promising candidate technology for edge AI computing. However, applying …
LSAC: A Low-Power Adder Tree for Digital Computing-in-Memory by Sparsity and Approximate Circuits Co-Design
The energy-efficient computing-in-memory (CIM) architectures have drawn much attention
as the increasing demands of neural networks. Several SRAM-based CIM architectures …
as the increasing demands of neural networks. Several SRAM-based CIM architectures …
Input/mapping precision controllable digital CIM with adaptive adder tree architecture for flexible DNN inference
Digital compute-in-memory (CIM) systems, known for their precise computations, have
emerged as a viable solution for real-time deep neural network (DNN) inference. However …
emerged as a viable solution for real-time deep neural network (DNN) inference. However …
A 28-nm Floating-Point Computing-in-Memory Processor Using Intensive-CIM Sparse-Digital Architecture
Computing-in-memory (CIM) chips have demonstrated promising high energy efficiency on
multiply–accumulate (MAC) operations for artificial intelligence (AI) applications. Though …
multiply–accumulate (MAC) operations for artificial intelligence (AI) applications. Though …