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Throughout the last decade, the microprocessor industry has been struggling to preserve the benefits of Moore's Law scaling. The persistent scaling of CMOS technology no longer yields exponential performance gains due in part to the growth of dark silicon. With each subsequent technology node generation, power constraints resulting from factors such as sub-threshold leakage currents are projected...
Numerous research efforts are targeting new devices that could continue performance scaling trends associated with Moore's Law and/or accomplish computational tasks with less energy. One such device is the ferroelectric FET (FeFET), which offers the potential to be scaled beyond the end of the silicon roadmap as predicted by ITRS. Furthermore, the Ids vs. Vgs characteristics of FeFETs may allow a...
Cellular neural networks (CNNs) are a powerful analog architecture that can outperform traditional von Neumann architecture for spatio-temporal information processing applications, e.g., image processing and speech recognition. Much existing work reports energy dissipation for CNNs at the chip level, which includes dissipation of sensors, actuators, and other components. As such, the impacts of various...
Novel devices are under investigation to extend the performance scaling trends that have long been associated with Moore's Law-based device scaling. Among the emerging devices being studied, tunnel FETs (or TFETs) are particularly attractive, especially when targeting low power systems. This paper studies the potential of analog/mixed-signal information processing using TFETs. The design of a highly-parallel...
Traditional CMOS based von Neumann architectures face daunting challenges in performing complex computational tasks at high speed and with low power on spatio-temporal data, e.g., image processing, pattern recognition, etc. In this study, we discuss the utilities of various steep slope, beyond-CMOS emerging devices for image processing applications within the non-von Neumann computing paradigm of...
A Cellular Neural Network (CNN) is a highly-parallel, analog processor that can significantly outperform von Neumann architectures for certain classes of problems. Here, we show how emerging, beyond-CMOS devices could help to further enhance the capabilities of CNNs, particularly for solving problems with non-binary outputs. We show how CNNs based on devices such as graphene transistors — with multiple...
Nanomagnetic logic (NML) is a “beyond-CMOS” technology that combines logic and memory capabilities through field-coupled interactions between nanoscale magnets. NML is intrinsically non-volatile, low-power, and radiation-hard when compared to CMOS equivalents. Moreover, there have been numerous demonstrations of NML circuit functionality within the last decade. These fabricated structures typically...
It is well known that CMOS scaling trends are now accompanied by less desirable byproducts such as increased energy dissipation. To combat the aforementioned challenges, solutions are sought at both the device and architectural levels. With this context, this work focuses on embedding a low voltage device, a Tunneling Field Effect Transistor (TFET) within a Cellular Neural Network (CNN) — a low power...
We present recent results on implementing logic using physically- coupled nanomagnet arrays. The binary state of a bit is represented by the magnetization state of a single-domain nanomagnet element, and logic is accomplished through direct physical interactions between them. We refer to this approach as nanomagnet logic (NML). We have demonstrated that NML satisfies the requirements for digital logic,...
Field-coupled nanomagnets can offer significant energy savings at iso-performance versus CMOS equivalents. Magnetic logic could be integrated with CMOS, operate in environments that CMOS cannot, and retain state without power. Clocking requirements lead to inherently pipelined circuits, and high throughput further improves application-level performance. However, bit conflicts — that will occur in...
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