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IEEE International Parallel and Distributed Processing Symposium (IPDPS)

SRNoC: A Statically-Scheduled Circuit-Switched Superconducting Race Logic NoC

George Michelogiannakis, Darren Lyles, Patricia Gonzalez-Guerrero, Meriam Gay Bautista, Dilip P. Vasudevan, Anastasiia Butko

Published May 1, 2021• 8 citations
Publisher

Abstract

Temporal encoding has been shown to be a natural fit for single flux quantum (SFQ) superconducting computing since SFQ already encodes information with the presence or absence of voltage pulses. However, past work in SFQ has focused on binary-encoded networks on chip (NoCs). In this paper, we propose superconducting rotary NoC (SRNoC), a NoC where both data and control paths operate in the temporal domain following the race logic (RL) convention. Therefore, SFQ chips with temporal compute or memory can use SRNoC to avoid converting between the temporal and binary domains that would result from using a binary-encoded NoC. Using RL also enables SRNoC to be area-efficient, mitigating SFQ technology's low device density. SRNoC treats pulses as independent packets and delivers them to outputs without changing their value, i.e. preserving the RL convention. SRNoC operates on a fixed, rotating connection schedule between inputs and outputs. In each connection window, multiple pulses (packets) can be transmitted sequentially. SRNoC provides 13.1x higher throughput per port per Josephson junction (JJ) compared to the best-performing of three demonstrated NoCs.

DOI
10.1109/ipdps49936.2021.00113

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Temporal encoding has been shown to be a natural fit for single flux quantum (SFQ) superconducting computing since SFQ already encodes information with the presence or absence of voltage pulses. However, past work in SFQ has focused on binary-encoded networks on chip (NoCs). In this paper, we propose superconducting rotary NoC (SRNoC), a NoC where both data and control paths operate in the temporal domain following the race logic (RL) convention. Therefore, SFQ chips with temporal compute or memory can use SRNoC to avoid converting between the temporal and binary domains that would result from using a binary-encoded NoC. Using RL also enables SRNoC to be area-efficient, mitigating SFQ technology's low device density. SRNoC treats pulses as independent packets and delivers them to outputs without changing their value, i.e. preserving the RL convention. SRNoC operates on a fixed, rotating connection schedule between inputs and outputs. In each connection window, multiple pulses (packets) can be transmitted sequentially. SRNoC provides 13.1x higher throughput per port per Josephson junction (JJ) compared to the best-performing of three demonstrated NoCs.
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.
This paper presents a superconducting, magnetically-coupled, shuttle-flux shift register (SF-SR) that stores single flux quantum (SFQ) pulses. This shift register has a DC bias operating margin of ±34% at 10 GHz, with a power dissipation of3.6 μW3.6~\mu W3.6 μWand 38% fewer Josephson junctions (JJs) when scaled up to multiple stages compared to a data flip-flop (DFF) based shift register. The clock input is inductively coupled and is independent from the data input. We then present three applications for our SF-SR. In the first application, we add two non-destructive readout (NDRO) cells to construct a buffer that temporarily stores the temporal information of a series of race logic (RL) pulses. The second application is a pseudo-random number generator based on a linear function shift register (LFSR). The third application is N parallel SF-SRs that can act similar to a deserializer or instead can emulate a single SF-SR of N times higher clock frequency. These three applications motivate deep shift registers with many shifting intervals, which our SF-SR can implement with fewer JJs and lower power consumption compared to DFF-based shift registers.
This paper presents a superconducting magnetically coupled shift buffer based on a shuttle-flux shift register that stores single flux quantum (SFQ) pulses. The shift register has a DC bias operating margin of ±34% at 10 GHz, the power dissipation of 3.6µW at the clock bias, and 38% fewer Josephson junctions for 1024 stages compared to DFF-based shift registers. Clock bias is independent of the input pulse sequence through inductive coupling with a minimal current drive. Based on our shift register, we add two non-destructive readouts (NDRO) cells to construct a buffer that can inexpensively store the temporal information of race logic (RL) pulses or a pulse train for spiking neural networks. These applications motivate deep shift registers with many shifting intervals, which our shift register can implement with only a pair of coupled inductors and one JJ per shifting interval.
Superconducting digital computing (SDC) based on Josephson junctions (JJs) offers significant potential for enhancing compute throughput and reducing energy consumption compared to conventional room-temperature CMOS-based approaches. Current superconducting logic families exhibit diverse characteristics in clocking strategies, power management, and information encoding techniques. This paper reviews recent advancements in unconventional computing methods specifically designed for superconducting digital circuits, emphasizing temporal computing and pulse-train representations. Notable techniques include race logic (RL), temporal pulse train computing (U-SFQ), and temporal multipliers, each offering unique performance and area advantages suited to superconducting implementations. Additionally, this paper reviews innovations in superconducting coarse-grain reconfigurable architectures (CGRA), superconducting-specific on-chip communication architectures, cryogenic sensor interfaces, and quantum computing control electronics. Finally, we highlight research challenges that should be addressed to facilitate the widespread adoption of superconducting digital computing.
In superconducting circuits, information is carried by ps-wide, µV-tall, Single Flux Quanta (SFQ) pulses. These circuits can operate at frequencies of hundreds of GHz with orders of magnitude lower switching energy than complementary-metal-oxide-semiconductors (CMOS). However, under the stringent area constraints of modern superconductor technologies, fully-fledged, CMOS-inspired superconducting architectures cannot be fabricated at large scales. Unary SFQ (U-SFQ) is an alternative computing paradigm that addresses these area constraints. In U-SFQ, information is mapped to a combination of streams of SFQ pulses and in the temporal domain. In this work, we propose a U-SFQ Convolutional Neural Network (CNN) hardware accelerator capable of comparable peak performance with state-of-the-art superconducting binary (B-SFQ) approaches in 32× less area. CNNs can operate with 5 to 8 bits of resolution with no significant degradation in classification accuracy. The proposed CNN accelerator effortlessly supports this variable resolution and, for less than 7 bits, yields 5×-63× better performance than CMOS and 15×-173× better area efficiency than B-SFQ.
A single-flux-quantum (SFQ) device provides fast operation with low power consumption. As SFQ devices can be applied in network switches requiring high throughput, we have been developing an SFQ network switch system. Not only the switch fabric but also the switch scheduler is a key component for a high-throughput network switch. The switch scheduler orders multiple inputs to a single output at the same time to prevent collisions from occurring in the switch fabric. The scheduling time becomes longer as the number of switch ports increases, because the scheduler must scan requests from all input ports. We designed two types of 4-port arbiters (i.e. switch scheduler sub-circuits) with structures based on serial or parallel processing. We chose to implement and demonstrate the serial arbiter because it has a smaller number of gates. It consisted of about 1800 Josephson junctions, including on-chip test circuitry. We tested a fabricated circuit at high speed and confirmed its correct operation at 40 GHz.