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This article presents a fully autonomous system-on-chip (SoC) that can be distributed along a fiber strand, capable of simultaneously harvesting energy, cooperatively scaling performance, sharing power, and booting-up with other in-fiber SoCs for ultra-low-power (ULP) sensing applications. Utilizing a custom switched capacitor energy harvesting and power management unit (EHPMU), the SoC can efficiently redistribute and reuse harvested energy along the fiber. Integrated on-chip, the ULP RISC-V digital core and temperature sensor enable energy-efficient sensing and computation at nanowatt power levels. A dedicated ripple boot-up and cooperative dynamic voltage and frequency scaling (DVFS) further optimize the operation and physical size of the system. Fabricated in 65 nm, measurement results show that the proposed SoC achieves 33 nW power consumption for the whole chip under 92 Lux lighting condition and can reduce control power down to 2.7 nW for the EHPMU. With the proposed power sharing and cooperative DVFS techniques, the SoC reduces the illuminance needed to stay alive by >7× down to 12 Lux. Integrated into a mm-scale polymer fiber, our SoC demonstrates the feasibility of fully autonomous and ULP on-body sensing systems in resource-constrained fiber environments.

Not all computing problems are created equal. The inherent complexity of processing certain classes of problems using digital computers has inspired the exploration of alternate computing paradigms. Coupled oscillators exhibiting rich spatio-temporal dynamics have been proposed for solving hard optimization problems. However, the physical implementation of such systems has been constrained to small prototypes. Consequently, the computational properties of this paradigm remain inadequately explored. Here, we demonstrate an integrated circuit of thirty oscillators with highly reconfigurable coupling to compute optimal/near-optimal solutions to the archetypally hard Maximum Independent Set problem with over 90% accuracy. This platform uniquely enables us to characterize the dynamical and computational properties of this hardware approach. We show that the Maximum Independent Set is more challenging to compute in sparser graphs than in denser ones. Finally, using simulations we evaluate the scalability of the proposed approach. Our work marks an important step towards enabling application-specific analog computing platforms to solve computationally hard problems.