Titlebook: NANO-CHIPS 2030; On-Chip AI for an Ef Boris Murmann,Bernd Hoefflinger Book 2020 Springer Nature Switzerland AG 2020 Nano-Electronics.Artifi

atopic-rhinitis

Acetabulum

The Memory Challenge in Ultra-Low Power Deep Learning,have storage requirements for both weights and activations that are often not compatible with on-chip memories and/or low cost, low power external memory options. In this chapter, we outline key ideas, recent achievements, and directions for future research towards taming the memory bottleneck for ultra-low power ML circuits and systems.

GLEAN

FLORA

连锁

Silicon Complementary MOS into Its 7th Decade,imum parasitics, best energy efficiency and >300 GHz bandwidth. In its sub-threshold mode-of-operation, it offers the most eye-like image sensor, and it allows robust logic operation down to 200 mV. The key future challenge is the efficient 3D integration of trillions of these ideal transistors into intelligent networks.

我怕被刺穿

scrutiny

SUGAR

Machine Learning at the Edge, chapter reviews state-of-the-art approaches and trends for low-energy machine learning inference and training at the edge. It covers dataflow, architecture and circuit design aspects of fully digital processors and mixed analog/digital implementations targeting the microwatts and milliwatts range.

Adrenaline

Nanolithography, opportunities for Nano-Imprint, Multiple-Electron-Bean Direct Writing and Directed Self-Assembly. To support the major drive towards 3D integration, new patterning and precision-alignment techniques will be introduced (Chaps. .–., . and .).

Compass