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Superconducting technology is a prime candidate for the future of computing. However, current superconducting prototypes are limited to small-scale examples due to stringent area constraints and complex architectures inspired from voltage-level encoding in CMOS; this is at odds with the 𝑝𝑠-wide Single Quantum Flux (SFQ) pulses used in superconductors to carry information. In this work, we propose a wave-pipelined Unary SFQ (U-SFQ) architecture that leverages the advantages of two data representations: pulse-streams and Race Logic (RL). We introduce novel building blocks such as multipliers, adders, and memory cells, which leverage the natural properties of SFQ pulses to mitigate area constraints. We then design and simulate three popular hardware accelerators: i) a Processing Element (PE), typically used in spatial architectures; ii) A dot-product-unit (DPU), one of the most popular accelerators in artificial neural networks and digital signal processing (DSP); and iii) A Finite Impulse Response (FIR) filter, a popular and computationally demanding DSP accelerator. The proposed U-SFQ building blocks require up to 200× fewer JJs compared to their SFQ binary counterparts, exposing an area-delay trade-off. This work mitigates the stringent area constraints of superconducting technology. CCS CONCEPTS• Hardware → Emerging technologies; • Theory of computation → Modal and temporal logics; • Computer systems organization → Architectures.

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Temporal computing promises to mitigate the stringent area constraints and clock distribution overheads of traditional superconducting digital computing. To design a scalable, area-and power-efficient superconducting network on chip (NoC), we propose packet-switched superconducting temporal NoC (PaST-NoC). PaST-NoC operates its control path in the temporal domain using race logic (RL), combined with bufferless deflection flow control to minimize area. Packets encode their destination using RL and carry a collection of data pulses that the receiver can interpret as pulse trains, RL, serialized binary, or other formats. We demonstrate how to scale up PaST-NoC to arbitrary topologies based on 2×2 routers and 4×4 butterflies as building blocks. As we show, if data pulses are interpreted using RL, PaST-NoC outperforms state-of-the-art superconducting binary NoCs in throughput per area by as much as 5× for long packets.