An overview of processing-in-memory circuits for artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …
such as visual recognition, natural language processing, autonomous vehicles, and …
Trending IC design directions in 2022
For the non-stop demands for a better and smarter society, the number of electronic devices
keeps increasing exponentially; and the computation power, communication data rate, smart …
keeps increasing exponentially; and the computation power, communication data rate, smart …
A 28nm 29.2 TFLOPS/W BF16 and 36.5 TOPS/W INT8 reconfigurable digital CIM processor with unified FP/INT pipeline and bitwise in-memory booth multiplication for …
Many computing-in-memory (CIM) processors have been proposed for edge deep learning
(DL) acceleration. They usually rely on analog CIM techniques to achieve high-efficiency NN …
(DL) acceleration. They usually rely on analog CIM techniques to achieve high-efficiency NN …
Diana: An end-to-end hybrid digital and analog neural network soc for the edge
DIgital-ANAlog (DIANA), a heterogeneous multi-core accelerator, combines a reduced
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
DIANA: An end-to-end energy-efficient digital and ANAlog hybrid neural network SoC
Energy-efficient matrix-vector multiplications (MVMs) are key to bringing neural network
(NN) inference to edge devices. This has led to a wide range of state-of-the-art MVM …
(NN) inference to edge devices. This has led to a wide range of state-of-the-art MVM …
A 28nm 15.59 µJ/token full-digital bitline-transpose CIM-based sparse transformer accelerator with pipeline/parallel reconfigurable modes
Transformer models have achieved state-of-the-art results in many fields, like natural
language processing and computer vision, but their large number of matrix multiplications …
language processing and computer vision, but their large number of matrix multiplications …
TD-SRAM: Time-domain-based in-memory computing macro for binary neural networks
In-Memory Computing (IMC), which takes advantage of analog multiplication-accumulation
(MAC) insides memory, is promising to alleviate the Von-Neumann bottleneck and improve …
(MAC) insides memory, is promising to alleviate the Von-Neumann bottleneck and improve …
A 8-b-precision 6T SRAM computing-in-memory macro using segmented-bitline charge-sharing scheme for AI edge chips
Advances in static random access memory (SRAM)-CIM devices are meant to increase
capacity while improving energy efficiency (EF) and reducing computing latency (). This …
capacity while improving energy efficiency (EF) and reducing computing latency (). This …
A heterogeneous in-memory computing cluster for flexible end-to-end inference of real-world deep neural networks
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high
computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile …
computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile …
ReDCIM: Reconfigurable digital computing-in-memory processor with unified FP/INT pipeline for cloud AI acceleration
Cloud AI acceleration has drawn great attention in recent years, as big models are
becoming a popular trend in deep learning. Cloud AI runs high-efficiency inference, high …
becoming a popular trend in deep learning. Cloud AI runs high-efficiency inference, high …