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CENTRALE LYON

CENTRALE LYON - Post-Doctoral Position Ferroelectric-Based Ternary Computing: From Circuit Design to System-Level Integration Scientific Context

Ecully, Auvergne-Rhône-Alpes, France · On-site · fulltime_fixed_term · INL - Institut des Nanotechnologies de Lyon

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About the role

Energy efficiency remains one of the foremost challenges in modern computing, spanning from edge IoT devices to high-performance data centers. Conventional CMOS-based architectures are approaching fundamental physical limits, while the "memory wall" — data transfers between processor and memory accounting for 70 to 90% of total system energy — continues to worsen with the ever-growing demands of data-intensive workloads such as deep neural networks and signal processing pipelines. In-Memory Computing (IMC) has emerged as a disruptive paradigm to overcome these bottlenecks by embedding arithmetic operations directly within memory arrays, drastically reducing data movement. In this landscape, ferroelectric field-effect transistors (FeFETs) stand out as particularly compelling devices: fully compatible with standard CMOS fabrication processes, non-volatile, reconfigurable, and capable of storing intermediate polarization states — a property that naturally enables ternary logic within a single device. The Post-Doctoral position will cover at least one of the following tasks: - Design Standard Cell Technology Library based on FeFET devices developed at INL in order to be used with synthesis flow. - Design and optimize ternary arithmetic units (multiply-accumulate units, adders, comparators) based on the FeFET ternary gate library developed within the project, targeting area, power, and timing closure under realistic process constraints. - Develop and refine system-level models of FeFET-based ternary IMC units, abstracting circuit-level characterization results into architecture simulators, enabling design space exploration across the full heterogeneous system. - Investigate hardware/software co-design strategies for mapping real-world applications — particularly convolutional neural networks (CNNs) and signal processing kernels — onto the ternary IMC fabric, exploiting approximate computing techniques to trade precision for energy efficiency. - Assess and benchmark the energy, performance, and accuracy trade-offs of ternary IMC against binary CMOS and binary NVM-based IMC reference implementations, using both synthetic benchmarks and real application workloads. - Contribute to the definition of a design methodology for ternary ferroelectric circuits, including EDA tool flows, cell library characterization guidelines, and design rules, with the aim of enabling broader community adoption.