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Zijia Su

Publications and source records attributed to Zijia Su.

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Nonvolatile single-ion memory with picosecond switching

The rapid development of artificial intelligence (AI), Internet of Things (IoT), and edge computing applications has posed severe challenges to conventional memory technologies in terms of density, speed, and energy consumption. Herein, a single-ion transport mechanism is proposed to achieve picosecond (ps) switching capability. For monolayer hexagonal boron nitride (h-BN) with single-atom vacancy defects, first-principles calculations reveal that single-ion penetration across the BN plane dominates the resistive switching. The trapping and release of a single ion correspond to different states of the memory device for one bit of information. Experimentally fabricated single-ion memory exhibits nonvolatile resistive switching with ultra-fast switching speed of 20 ps and ultra-low energy consumption of 310 aJ/bit. This high performance is attributed to the extremely short distance for the single ion to travel through. Such devices pave the way for the realization of high-performance nonvolatile memory with ultra-fast speed, ultra-low energy consumption, and high storage density, that is called the "Unified Memory" long desired by the whole industry.

physics.app-ph

YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI

In this paper, we further explore the potential of analog in-memory computing (AiMC) and introduce an innovative artificial intelligence (AI) accelerator architecture named YOCO, featuring three key proposals: (1) YOCO proposes a novel 8-bit in-situ multiply arithmetic (IMA) achieving 123.8 TOPS/W energy-efficiency and 34.9 TOPS throughput through efficient charge-domain computation and timedomain accumulation mechanism. (2) YOCO employs a hybrid ReRAM-SRAM memory structure to balance computational efficiency and storage density. (3) YOCO tailors an IMC-friendly attention computing flow with an efficient pipeline to accelerate the inference of transformer-based AI models. Compared to three SOTA baselines, YOCO on average improves energy efficiency by up to 3.9x-19.9x and throughput by up to 6.8x-33.6x across 10 CNN/transformer models.

cs.AR