Stochastic approaches for effective power distribution network optimization are proposed. Considering node voltages obtained using dynamic voltage drop analysis as sample variables, multi-variate regression is conducted to optimize clock timing metrics, such as clock skew or jitter. Aggregate correlation coefficient (ACC) which quantifies connectivity between different chip regions is defined in order to find a possible insufficiency in wire connections of a power distribution network. Based on the ACC, we also propose a procedure using linear regression to find the most effective region for improving clock timing metrics. By using the proposed procedure, effective fixing point were obtained two orders faster than by using brute force circuit simulation.

This paper concerns a new model for estimating the wire length distribution (WLD) of a system-on-a-chip (SoC). The WLD represents the correlation between wire length and the number of interconnects, and we can predict circuit performances such as power consumption, maximum clock frequency, and chip size from the WLD. A WLD model considering core utilization has been proposed, and the core utilization has a large impact on circuit performance. However, the WLD model can treat only a one-function circuit. We propose a new WLD model considering core utilization to estimate the wire length distribution of SoC, which consists of several different-function macroblocks. We present an optimization method to determine each core utilization of macroblocks.

A novel voltage measurement circuit which utilizes process variation is proposed. Using the proposed circuit, the voltage of a nonperiodic waveform at a particular time point can be accurately captured by a single clock pulse (one-shot measurement). The proposed circuit can be designed without compensation circuits against process variation, and thus occupies only a small area. An analytical expression of offset voltage for the comparator utilizing process variation (UPV-comparator), which plays a key role in the proposed circuit, is derived and design considerations for the proposed circuit are discussed. The circuit operation is confirmed through SPICE simulation using 90 nm CMOS device models. The -0.04 and -3 dB bandwidths (99% and 50% amplitudes) of the proposed circuit are about 10 MHz and far over 1 GHz, respectively. The circuit area is also estimated using an experimental layout.

A time-slicing ring oscillator (TSRO) which captures time-dependent delay degradation due to periodic transient voltage drop on a power supply network is proposed. An impact of the supply voltage fluctuations, including voltage drop and overshoot, on logic circuit delay is evaluated as a change of oscillation frequency. The TSRO is designed using standard logic cells so that it can be placed almost anywhere in a digital circuit wherein supply voltage fluctuation is concerned. We also propose a new procedure for reconstructing supply voltage waveform. The procedure enables us to accurately monitor time-dependent, effective supply voltages. The -1 dB bandwidth of the TSRO is simulated to be 15.7 GHz, and measured time resolution is 131 ps. Measurement results of a test chip using 90-nm standard CMOS process successfully proved the feasibility of both delay degradation and effective supply voltage fluctuation measurements. Measurement of spatial voltage drop fluctuation is achieved.

The reliability of deep neural networks (DNN) against hardware errors is essential as DNNs are increasingly employed in safety-critical applications such as automatic driving. Transient errors in memory, such as radiation-induced soft error, may propagate through the inference computation, resulting in unexpected output, which can adversely trigger catastrophic system failures. As a first step to tackle this problem, this paper proposes constructing a vulnerability model (VM) with a small number of fault injections to identify vulnerable model parameters in DNN. We reduce the number of bit locations for fault injection significantly and develop a flow to incrementally collect the training data, i.e., the fault injection results, for VM accuracy improvement. We enumerate key features (KF) that characterize the vulnerability of the parameters and use KF and the collected training data to construct VM. Experimental results show that VM can estimate vulnerabilities of all DNN model parameters only with 1/3490 computations compared with traditional fault injection-based vulnerability estimation.

In this paper, we propose a via distribution model for yield estimation. This model expresses a relationship between the number of vias and wire length. We also provide an estimate for the total number of vias in a circuit, derived from the via distribution and the wire-length distribution. The via distribution is modeled as a function of track utilization, and the wire-length distribution can be derived from the gate-level netlist and the layout area. We extract model parameters from the commercial chips designed for 0.18-µm and 0.13-µm CMOS processes, and demonstrate the yield degradation caused by vias.