In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

Large-scale field-programmable analog array (FPAA) devices could enable ubiquitous analog or mixed-signal low-power sensor to processing devices similar to the ubiquitous implementation of the existing field-programmable gate array (FPGA) devices. Design tools enable high-level synthesis to gate/transistor design targeting today's FPGA devices and the opportunity for analog or mixed-signal applications with FPAA devices. This discussion will illustrate the FPAA concepts and FPAA history. The development of FPAAs enables the development of multiple potential metrics, and these metrics illustrate future FPAA device directions. The system-on-chip (SoC) FPAA devices illustrate the IC capabilities, computation, tools, and resulting hardware infrastructure. SoC FPAA device generation has enabled analog computing with levels of abstraction for application design.

This paper presents an analog-digital hardware-software co-design environment for simulating and programming reconfigurable systems. The tool simulates, designs, as well as enables experimental measurements after compiling to configurable systems in the same integrated design tool framework. High level software in Scilab/Xcos (open-source programs similar to MATLAB/Simulink) that converts the high-level block description by the user to blif format (sci2blif), which acts as an input to the modified VPR tool, including the code v p r 2 s w c s , encoding the specific platform through specific architecture files, resulting in a targetable switch list on the resulting configurable analog–digital system. The resulting tool uses an analog and mixed-signal library of components, enabling users and future researchers access to the basic analog operations/computations that are possible.

In this paper, we demonstrate how a network of dendrites can be used to build the state decoding block of a wordspotter similar to a Hidden Markov Model (HMM) classifier structure. We present simulation and experimental data for a single line dendrite and also experimental results for a dendrite-based classifier structure. This work builds on previously demonstrated building blocks of a neural network: the channel, synapses and dendrites using CMOS circuits. These structures can be used for speech and pattern recognition. The computational efficiency of such a system is >10 MMACs/μW as compared to Digital Systems which perform 10 MMACs/mW.

This paper presents scaling of Floating-Gate (FG) devices, and the resulting implication to large-scale Field Programmable Analog Arrays (FPAA) systems. The properties of FG circuits and systems in one technology (e.g., 350 nm CMOS) are experimentally shown to roughly translate to FG circuits in scaled down processes in a way predictable through MOSFET physics concepts. Scaling FG devices results in higher frequency response, (e.g., FPAA fabric) as well as lower parasitic capacitance and lower power consumption. FPAA architectures, limited to 50–100 MHz frequency ranges could be envisioned to operate at 500 MHz–1 GHz for 130 nm line widths, and operate around 4 GHz for 40 nm line widths.

This discussion develops a theoretical analog architecture framework similar to the well developed digital architecture theory. Designing analog systems, whether small or large scale, must optimize their architectures for energy consumption. As in digital systems, a strong architecture theory, based on experimental results, is essential for these opportunities. The recent availability of programmable and configurable analog technologies, as well as the start of analog numerical analysis, makes considering scaling of analog computation more than a purely theoretical interest. Although some aspects nicely parallel digital architecture concepts, analog architecture theory requires revisiting some of the foundations of parallel digital architectures, particularly revisiting structures where communication and memory access, instead of processor operations, that dominates complexity. This discussion shows multiple system examples from Analog-to-Digital Converters (ADC) to Vector-Matrix Multiplication (VMM), adaptive filters, image processing, sorting, and other computing directions.