Abstract
Abstract Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.
Highlights
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics
A better solution could be Phase-change memory (PCM) directly embedded with the logic itself (BEOL) without any interconnect bottleneck and eventually we could foresee full-fledged non-von Neumann accelerator chips where the embedded PCM is used for analogue in-memory computing
Besides the expected prospective realization of densely integrated non-volatile and ultra-low-power ferroelectric memories in near future, this development directly leads to the adoption of the trinity of ferroelectric memory devices – ferroelectric capacitor (FeCAP), field effect transistors (FeFET) and ferroelectric tunneling junction (FTJ) - for beyond von Neumann computing
Summary
One of the key properties of PCM that enables such inmemory computing (IMC) is the ability to store two levels of resistance/conductance values in a non-volatile manner and to reversibly switch from one level to the other (binary storage capability) This property facilitates in-memory logical operations enabled through the interaction between the voltage and resistance state variables [3]. Applications of in-memory logic include database query [4] and hyper-dimensional computing [5] Another key property of PCM that enables IMC is its ability to achieve not just two levels but a continuum of resistance values (analogue storage capability) [1]. Concluding Remarks The non-volatile binary storage, analogue storage and accumulative behaviour associated with PCM devices can be exploited to perform in-memory computing. A better solution could be PCM directly embedded with the logic itself (BEOL) without any interconnect bottleneck and eventually we could foresee full-fledged non-von Neumann accelerator chips where the embedded PCM is used for analogue in-memory computing
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