Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W–nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead.

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

Current research in the area of Neural Networks (NN) has resulted in performance advancements for a variety of complex problems. Especially, embedded system applications rely more and more on the utilization of convolutional NNs to provide services such as image/audio classification and object detection. The core arithmetic computation performed during NN inference is the multiply-accumulate (MAC) operation. In order to meet tighter and tighter throughput constraints, NN accelerators integrate thousands of MAC units resulting in a significant increase in power consumption. Approximate computing is established as a design alternative to improve the efficiency of computing systems by trading computational accuracy for high energy savings. In this work, we bring approximate computing principles and NN inference together by designing NN specific approximate multipliers that feature multiple accuracy levels at run-time. We propose a time-efficient automated framework for mapping the NN weights to the accuracy levels of the approximate reconfigurable accelerator. The proposed weight-oriented approximation mapping is able to satisfy tight accuracy loss thresholds, while significantly reducing energy consumption without any need for intensive NN retraining. Our approach is evaluated against several NNs demonstrating that it delivers high energy savings (17.8% on average) with a minimal loss in inference accuracy (0.5%).

Negative capacitance field-effect transistor (NCFET) addresses one of the key fundamental limits in technology scaling, akin to the non-scalable Boltzmann factor, by offering a sub-threshold swing below 60 mV/decade. In this paper, we investigate how the NCFET technology can open the doors not only for the continuation of Moore’s law, which is approaching its end, but also for reviving Dennard’s scaling, which stopped more than a decade ago. We study NCFET for the 7-nm FinFET technology node, from physics to processors and demonstrate that prior trends in processor design with respect to voltage and frequency can be revived with the NCFET technology. Our work focuses on answering the following three questions towards drawing the impact of NCFET technology on computing efficiency: In how far NCFET technology will enable processors: 1) to operate at higher frequencies without increasing voltage; 2) to operate at higher frequencies without increasing power density, which is substantial, because maintaining on-chip power densities under tight constraints due to limited cooling capabilities is inevitable; and 3) to operate at lower voltages, while still fulfilling performance requirements, which is substantial for the emerging Internet of Things, in which available power budgets for such devices are typically very restricted.

In this work, we investigate for the first time the impact of Negative Capacitance FinFET (NC-FinFET) technology on the performance of processors under the effects of process variations for various operating voltages. The industry compact model of FinFETs (BSIM-CMG) is fully calibrated to reproduce Intel 14nm FinFET data of high volume manufacturing process. A physics-based negative capacitance (NC) model is integrated and solved self-consistently within the BSIM-CMG model. This allows the creation of NC-FinFET standard cell libraries, while considering the effects of various variability sources both in the ferroelectric layer as well as in the underlying constituent FinFET device. The variability-aware NC-FinFET libraries, fully compatible with the existing standard design flow of circuits, are then employed to perform simulations using commercial statistical timing analysis tools in order to study the performance of a 14nm processor. For comprehensive analysis and comparisons, our implementation is done for both NC-FinFET and conventional (baseline) FinFET for a wide range of voltages. Our results demonstrate that process variations have a larger impact on the processor's performance in NC-FinFET - when it operates at a lower voltage compared to the baseline FinFET that still operates at the nominal high voltage - due to the additional ferroelectric-induced variability. Results also reveal that neglecting process variations leads to overestimating the benefit that NC brings to the processor's frequency improvement because of the larger timing guardband that is needed to overcome variability in NC-FinFET.

A comprehensive simulation flow is demonstrated to assess the negative-bias temperature instability (NBTI) impact on the performance and power of digital logic circuits based on the 14-nm node FinFET technology. Fully calibrated technology computer-aided design simulations are used to determine the preaged and postaged device characteristics; the results are used for calibrating the BSIM-CMG compact model. Standard cell libraries are characterized next, by only threshold voltage shift (ΔV _T ) and by both ΔV _T and subthreshold slope shift (ASS). Various benchmark circuits are synthesized and analyzed, and their timing degradation is compared to ring oscillator results. The consequence of ignoring ASS on off current and static power (P _static ) is estimated.