The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …

The exponential increase in generated data as well as the advances in high-performance
computing has paved the way for the use of complex machine learning methods. Indeed, the …

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 …

The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …

Computing-in-memory (CIM) has shown high energy efficiency on low-precision integer
multiply-accumulate (MAC)[1–3]. However, implementing floating-point (FP) operations …

This article presents DynaPlasia, a reconfigurable eDRAM-based in-memory computing
(IMC) processor with a novel triple-mode cell. It enables higher system-level performance …

In-memory computing (IMC) processors show significant energy and area efficiency for deep
neural network (DNN) processing [1–3]. As shown in Fig. 16.5. 1, despite promising macro …

Emerging Artificial Intelligence-enabled Internet-of-Things (Al-loT) SoCs 1–4 for augmented
reality, personalized healthcare and nano-robotics need to run a large variety of tasks within …

In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …

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 …