Pass transistor logic (PTL) has emerged recently in advanced high-speed optical communication system due to its higher speed and lower power consumption compared to traditional CMOS logic. However, the sensitivity to radiation-induced soft errors of PTL implementations is significant different from CMOS circuitry, which emphasizes the need for understanding the mechanism of soft error propagation in PTL. Due to the non-conventional logic structure in PTL, previous approaches of pulse width modelling in CMOS logic are no more applicable since they are not always measurable. Hence, in this paper, we propose a learning-based structural regression modeling approach to explore the soft error propagation mechanism in PTL at transistor level. Our models can be easily mapped onto higher level to analyze soft error propagation in any complex PTL designs. The experimental results on a 4-bit ripple carry adder demonstrate that our models can achieve high accuracy compared with SPICE simulation.

Approximate computing is an emerging computing paradigm to design energy-efficient systems. Many greedy approximate logic synthesis (ALS) methods have been proposed to automatically synthesize approximate circuits. They typically need to consider all local approximate changes (LACs) in each iteration of the ALS flow to select the best one, which is time-consuming. In this paper, we propose SEALS, a Sensitivity-driven Efficient ALS method to speed up a greedy ALS flow. SEALS centers around a newly proposed concept called sensitivity, which enables a fast and accurate error estimation method and an efficient method to filter out unpromising LACs. SEALS can handle any statistical error metric. The experimental results show that it outperforms a state-of-the-art ALS method in runtime by 12X to 15X without reducing circuit quality.

With the slow-down of Moore's law and the increasing requirements on energy efficiency, alternative logic styles compared to complementary static CMOS have to be revisited for digital circuit implementations. Pass Transistor Logic (PTL) gained much attention in the '90s, however, only a limited number of recent investigations and publications regarding PTL exist that use advanced technology nodes. This paper compares key performance metrics of 22 different PTL based 1-bit full adder designs to a complementary static CMOS logic reference, using a recent 12nm FinFET technology. The figures of merit are the propagation delay, the energy consumption, and the energy-delay-product (EDP). Our investigations show that PTL based adder circuits can have an up to 49% decreased delay and a 48% and 63% reduced energy consumption and EDP, respectively, compared to a state-of-the-art complementary CMOS logic reference. In addition, we analyzed the impact of PVT variations on the delay for selected PTL full adder designs.

Recent advanced high-speed communication systems, such as optical systems, require highest reliability at lowest possible power consumption. Thus, Pass Transistor Logic (PTL) is gaining lots of interest in these communication systems due to its power saving potential compared to traditional CMOS logic. However, due to the non-conventional logic structure, its susceptibility to radiation-induced soft errors is different from CMOS circuitry. Due to the unique generation and propagation of Single Event Transients (SETs) in PTL, different approaches for PTL soft error rate (SER) estimation are required. In this paper we propose a machine learning (ML) approach for SET propagation in PTL logic. Multi-layer feed-forward neural network together with support vector classifier (SVC) are used to build the SET pulse width and pulse amplitude models. Bayesian optimization using Gaussian Processes is utilized to tune the hyperparameters of neural network. The experimental results on full adder (FA), which is the key component in many large cirucits such as ALU, and comparison with Monte Carlo (MC) spectre simulations confirm the accuracy and speed of the proposed method.

Approximate computing is an emerging strategy to improve the energy efficiency of many error-tolerant applications. To design an approximate circuit automatically, many approximate logic synthesis (ALS) methods have been proposed, among which many are greedy. To improve the synthesis quality of these greedy methods, one key is to calculate the errors of all candidate approximate transformations accurately. However, the traditional simulation-based method is time consuming. Instead, many existing methods just perform quick but inaccurate error estimation. In this work, to improve both the accuracy and runtime of error estimation, we propose VECBEE, a versatile efficiency–accuracy configurable batch error estimation method for greedy ALS. It is based on Monte Carlo simulation and an efficient technique to capture whether a signal change due to an introduced approximation will be propagated to each primary output. VECBEE is generally applicable to any statistical error measurement, such as error rate and average error magnitude, and any graph-based circuit representation. It allows a flexible tradeoff between the error estimation accuracy and the runtime, while even the fully accurate version is much faster than the traditional simulation-based method. We apply VECBEE to two representative greedy ALS methods and demonstrate its effectiveness in generating better approximate circuits. The code of VECBEE is made open source.