Abstract Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) 1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory 2–5 . Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware 6–17 , it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM—a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST 18 and 85.7 percent on CIFAR-10 19 image classification, 84.7-percent accuracy on Google speech command recognition 20 , and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task.
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Detecting Temporal Correlation on HfO 2 Based RRAM on 65nm CMOS Technology
Hafnium-oxide based bipolar RRAM was investigated for high-level temporal correlation detection, for in-memory computing. The experimental analog data of HfO2 RRAM, both in RESET and SET regimes was evaluated to detect 10 correlated processes from 25 processes on a 5x5 RRAM array. Our method gave 36,000-53,000 times less energy consumption than that of a previous implementation with phase change memory, and a predicted acceleration of 1600-2100 times the execution time than that of 1xPOWER8 CPU (1 thread) for detecting correlation between 25 processes.
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- Award ID(s):
- 1823015
- NSF-PAR ID:
- 10355311
- Date Published:
- Journal Name:
- 2022 IEEE 31st Microelectronics Design & Test Symposium (MDTS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Biomimetic synaptic processes, which are imitated by functional memory devices in the computer industry, are a key focus of artificial intelligence (AI) research. It is critical to developing a memory technology that is compatible with Brain-Inspired Computing in order to eliminate the von Neumann bottleneck that restricts the efficiency of traditional computer designs. Due to restrictions such as high operation voltage, poor retention capacity, and high power consumption, silicon-based flash memory, which presently dominates the data storage devices market, is having difficulty meeting the requirements of future data storage device development. The developing resistive random-access memory (RRAM) has sparked intense investigation because of its simple two-terminal structure: two electrodes and a switching layer. RRAM has a resistive switching phenomenon which is a cycling behavior between the high resistance state and the low resistance state. This developing device type is projected to outperform traditional memory devices. Indium gallium zinc oxide (IGZO) has attracted great attention for the RRAM switching layer because of its high transparency and high atomic diffusion property of oxygen atoms. More importantly, by controlling the oxygen ratio in the sputter gas, its electrical properties can be easily tuned. The IGZO has been applied to the thin-film transistor (TFT), thus, it is very promising to integrate RRAM with TFT. In this work, we proposed IGZO-based RRAMs. ITO was chosen as the bottom electrode towards achieving a fully transparent memristor. And for the IGZO switching layer, we varied the O2/Ar ratio during the deposition to modify the oxygen vacancy of IGZO. Through the XPS measurement, we confirmed that the higher O2/Ar ratio can lower the oxygen vacancy concentration. We also chose ITO as the top electrode, for the comparison, two active metals copper and silver were tested for the top electrode materials. For our IGZO layer, the best ratio of O2/Ar is the middle value. And copper top electrode device has the most stable cycling switching and the silver one is perfect for large memory window, however, it encounters a stability issue. The optical transmission examination was performed using a UV-Vis spectrometer, and the average transmittance of the complete devices in the visible-light wavelength range was greater than 90%, indicating good transparency. 50nm, 100nm, and 150nm RS layers of IGZO RRAM were produced to explore the thickness dependency on the characteristics of the RS layer. Also, because the oxygen vacancy concentration influences the RS and RRAM performance, the oxygen partial pressure during IGZO sputtering was modified to maximize the property. Electrode selection is critical and can have a significant influence on the device's overall performance. As a result, Cu TE was chosen for our second type of device because Cu ion diffusion can aid in the development of conductive filaments (CF). Finally, between the TE and RS layers, a 5 nm SiO2 barrier layer was used to limit Cu penetration into the RS layer. Simultaneously, this SiO2 inserting layer can offer extra interfacial series resistance in the device, lowering the off current and, as a result, improving the on/off ratio and overall performance. In conclusion, transparent IGZO-based RRAMs have been created. The thickness of the RS layer and the sputtering conditions of the RS layer were modified to tailor the property of the RS layer. A series of TE materials and a barrier layer were incorporated into an IGZO-based RRAM and the performance was evaluated in order to design the TE material's diffusion capabilities to the RS layer and the BE. Our positive findings show that IGZO is a potential material for RRAM applications and overcoming the existing memory technology limitation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MDTS54894.2022.9826965
