Researchers at CEA-Leti and Stanford University have developed the world’s first circuit integrating multiple-bit non-volatile memory (NVM) technology called Resistive RAM (RRAM) with silicon computing units, as well as new memory resiliency features that provide 2.3-times the capacity of existing RRAM. Target applications include energy-efficient, smart-sensor nodes to support artificial intelligence on the Internet of Things, or “edge AI”.
The proof-of-concept chip has been validated for a wide variety of applications (machine learning, control, security). Designed by a Stanford team led by Professors Subhasish Mitra and H.-S. Philip Wong and realized in CEA-Leti’s cleanroom in Grenoble, France, the chip monolithically integrates two heterogeneous technologies: 18 kilobytes (KB) of on-chip RRAM on top of commercial 130nm silicon CMOS with a 16-bit general-purpose microcontroller core with 8KB of SRAM.
The new chip delivers 10-times better energy efficiency (at similar speed) versus standard embedded FLASH, thanks to its low operation energy, as well as ultra-fast and energy-efficient transitions from on mode to off mode and vice versa. To save energy, smart-sensor nodes must turn themselves off. Non-volatility, which enables memories to retain data when power is off, is thus becoming an essential on-chip memory characteristic for edge nodes. The design of 2.3 bits/cell RRAM enables higher memory density (NVM dense integration) yielding better application results: 2.3x better neural network inference accuracy, for example, compared to a 1-bit/cell equivalent memory.
“The Stanford/CEA-Leti team demonstrated a complete chip that stores multiple bits per on-chip RRAM cell. Stored information is correctly processed when compared with previous demonstrations using standalone RRAM or a few cells in a RAM array,” said Thomas Ernst, Leti’s chief scientist for silicon components and technologies. “This multi-bit storage improves the accuracy of neural network inference, a vital component of AI.”
Mitra said the chip demonstrates several industry firsts for RRAM technology. These include new algorithms that achieve multiple bits-per-cell RRAM at the full memory level, new techniques that exploit RRAM features as well as application characteristics to demonstrate the effectiveness of multiple bits-per-cell RRAM at the computing system level, and new resilience techniques that achieve a useful lifetime for RRAM-based computing systems.
“This is only possible with a unique team with end-to-end expertise across technology, circuits, architecture, and applications,” he said. “The Stanford SystemX Alliance and the Carnot Chair of Excellence in NanoSystems at CEA-Leti enabled such a unique collaboration.”
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