Face retouching is one of the most time-consuming steps in professional photography pipelines. The existing auto-mated approaches blindly apply smoothing on the skin, destroying the delicate texture of the face. We present the first automatic face retouching approach that produces high-quality professional-grade results in less than two seconds. Unlike previous work, we show that our method preserves textures and distinctive features while retouching the skin. We demonstrate that our trained models generalize across datasets and are suitable for low-resolution cellphone images. Finally, we release the first large-scale, professionally retouched dataset with our baseline to encourage further work on the presented problem.

This paper presents a novel clustering based placement algorithm for single flux quantum (SFQ) family of super-conductive electronic circuits. In these circuits nearly all cells receive a clock signal and a placement algorithm that ignores the clock routing cost will not produce high quality solutions. To address this issue, proposed approach simultaneously minimizes the total wirelength of the signal nets and area overhead of the clock routing. Furthermore, construction of a perfect H-tree in SFQ logic circuits is not viable solution due to the resulting very high routing overhead and the in-feasibility of building exact zero-skew clock routing trees. Instead a hybrid clock tree must be used whereby higher levels of the clock tree (i.e., those closer to the clock source) are based on H-tree construction whereas lower levels of the clock tree follow a linear (i.e., chain-like) structure. The proposed approach is able to reduce the overall half-perimeter wirelength by 15% and area by 8% compared with state-of-the-art techniques.

Single flux quantum (SFQ) logic is a promising candidate to replace the CMOS logic for high speed and low power applications due to its superiority in providing high performance and energy efficient circuits. However, developing effective Electronic Design Automation (EDA) tools, which cater to special characteristics and requirements of SFQ circuits such as depth minimization and path balancing, are essential to automate the whole process of designing large SFQ circuits. In this paper, a novel technology mapping tool, called SFQmap, is presented, which provides optimization methods for minimizing first the circuit depth and path balancing overhead and then the worst-case stage delay of mapped SFQ circuits. Compared with the state-of-the-art technology mappers, SFQmap reduces the depth and path balancing overhead by an average of 14% and 31%, respectively.

Rapid Single Flux Quantum (RSFQ) logic cells have traditionally been limited to driving one fanout cell because of the difficulty in distributing the single flux quantum pulse to multiple fanouts. This paper presents a method to modify the standard RSFQ cells at their input/output interfaces to other cells in order to support a multiple-fanout drive capability. This capability is especially useful for clock distribution in RSFQ logic. This is because RSFQ logic is requires the clock signal to be provided to every logic gate. This is why this paper focuses on the clock signal driving more than one cell without the use of splitters. The potential tradeoff is in lower margins for the cells. However, by careful design of the RSFQ cells, the yield is not compromised by our proposed technique.

As a result of the Temperature Effect Inversion (TEI) in FinFET-based designs, gate delays decrease with the increase of temperature. In contrast, the resistive characteristic and hence delay of global interconnects increase with the temperature. However, as shown in this paper, if buffers are judiciously inserted in global interconnects, the buffer delay decrease is more pronounced than the interconnect delay increase, resulting in an overall performance improvement at higher temperatures. More specifically, this work models the delay of buffer-inserted global interconnects vs. temperature in order to derive the optimal number and size of buffers for a given interconnect length and temperature. Furthermore, the paper addresses the problem of minimizing the buffered interconnect energy consumption by changing the supply voltage level or FinFET threshold voltage, and also presents a temperature-aware optimization policy for solving this problem. Simulation results show average interconnect energy savings of 16% with no performance penalty for five different benchmarks implemented on a 14nm FinFET technology.

GPU adoption for general purpose computing has been accelerating. To support a large number of concurrently active threads, GPUs are provisioned with a very large register file (RF). The RF power consumption is a critical concern. One option to reduce the power consumption dramatically is to use near-threshold voltage(NTV) to operate the RF. However, operating MOSFET devices at NTV is fraught with stability and reliability concerns. The adoption of FinFET devices in chip industry is providing a promising path to operate the RF at NTV while satisfactorily tackling the stability and reliability concerns. However, the fundamental problem of NTV operation, namely slow access latency, remains. To tackle this challenge in this paper we propose to build a partitioned RF using FinFET technology. The partitioned RF design exploits our observation that applications exhibit strong preference to utilize a small subset of their registers. One way to exploit this behavior is to cache the RF content as has been proposed in recent works. However, caching leads to unnecessary area overheads since a fraction of the RF must be replicated. Furthermore, we show that caching is not efficient as we increase the number of issued instructions per cycle, which is the expected trend in GPU designs. The proposed partitioned RF splits the registers into two partitions: the highly accessed registers are stored in a small RF that switches between high and low power modes. We use the FinFET's back gate control to provide low overhead switching between the two power modes. The remaining registers are stored in a large RF partition that always operates at NTV. The assignment of the registers to the two partitions will be based on statistics collected by the a hybrid profiling technique that combines the compiler based profiling and the pilot warp profiling technique proposed in this paper. The partitioned FinFET RF is able to save 39% and 54% of the RF leakage and the dynamic energy, respectively, and suffers less than 2% performance overhead.

The objective of this paper is to minimize the energy-delay product of static random access memory (SRAM) arrays by using a device-circuit architecture co-optimization framework. More specifically, at the device-level, high-Vt FinFETs are adopted for the 6T SRAM cell, which significantly reduces the leakage power and improves static noise margins. However, due to the lower ON current, the bit-line delay of the read access is increased. Accordingly, at the circuit-level, the voltage level of assist circuits, and at the architecture-level (i.e., the array organization), key parameters of the SRAM array are jointly optimized to derive a design that results in the minimum energy-delay product point. By using the proposed optimization framework, for SRAM array capacities ranging from 1KB to 16KB, on average 59% lower energy-delay product with maximum 12% (and on average 9%) performance penalty is achieved.

This paper deals with obtaining the minimum operating voltage of memory arrays based on TFET SRAM cells. First, we compare the I-V characteristics of two TFETs and one FDSOI using SPICE simulations based on 20nm technology models. The results reveal that TFET devices exhibit high ON/OFF current ratios at different power supply voltage levels. This observation suggests a higher stability for SRAM cells based on these devices. Next, the characteristics of 6T SRAM cells implemented using minimum sized transistors based on these three device structures are compared. The comparison, which considers two TFET cell structures, i.e., inward and outward SRAMs, is performed at different supply voltages. The results for the hold static noise margin show that at low supply voltages (i.e., below 300mV), the FDSOI SRAM cell cannot hold data whereas both the inward and outward structures of TFET have acceptable noise margins at all supply voltages. Among the two TFET structures, the outward cell is selected because of higher speed especially for the write operation. TFET SRAMs suffer from long read access latency at ultra-low supply voltages (e.g., 150mV). The problem, however, may be overcome by using the negative GND read-assist technique. The results show that for a 32×32 TFET outward SRAM array, the minimum energy consumption (energy-delay product) may be achieved at the supply voltage of 200mV (300mV) with 1.32GHz (4.55GHz) as the read access frequency

A cross-layer framework (spanning device and circuit levels) is presented for designing robust and energy-efficient SRAM cells, made of deeply-scaled FinFET devices. In particular, 7nm FinFET devices are designed and simulated by using Synopsys TCAD tool suite, Sentaurus. Next, 6T and 8T SRAM cells, which are composed of these devices, are designed and optimized. To enhance the cell stability and reduce leakage energy consumption, the dual (i.e., front and back) gate control feature of FinFETs is exploited. This is, however, done without requiring any external signal to drive the back gates of the FinFET devices. Subsequently, the effect of process variations on the aforesaid SRAMs is investigated and steps are presented to protect the cells against these variations. More precisely, the SRAM cells are first designed to minimize the expected energy consumption (per clock cycle) subject to the non-destructive read and successful write requirements under worst-case process corner conditions. These SRAM cells, which are overly pessimistic, are then refined by selectively adjusting some transistor sizes, which in turn reduces the expected energy consumption while ensuring that the parametric yield of the cells remains above some prespecified threshold. To do this efficiently, an analytical method for estimating the yield of SRAM cells under process variations is also presented and integrated in the refinement procedure. A dual-gate controlled 6T SRAM cell operating at 324mV (in the near-threshold supply regime) is finally presented as a high-yield and energy-efficient memory cell in the 7nm FinFET technology.

The impact of dark silicon phenomenon on multicore processors under deeply-scaled FinFET technologies is investigated in this paper. To do this accurately, a cross-layer framework, spanning device, circuit, and architecture levels is initially introduced. Using this framework, leakage and dynamic power consumptions as well as frequency levels of in-order and out-of-order (OoO) processor cores, and on-chip cache memories and routers in a network-on-chip-based chip multiprocessor system synthesized in 7nm FinFET technology and operating in both super- and near-threshold voltage regimes are presented. Subsequently, total power consumptions of multicore chips manufactured with (i) OoO and (ii) in-order processor cores are reported and compared. According to our results, for a 64-core chip and 15W thermal design power budget, 64% and 39% dark silicon are observed in OoO and in-order multicores, respectively, under super-threshold regime. These percentages drop to 19% and 0% for OoO and in-order multicores operating in the near-threshold regime, respectively. Furthermore, the highest energy efficiencies are achieved by operating in the near-threshold regime, which points to the effectiveness of near-threshold computing in mitigating the effect of dark silicon phenomenon under deeply-scaled technologies.

Regular, local-neighbor topologies of quantum architectures restrict interactions to adjacent qubits, which in turn increases the latency of quantum circuits mapped to these architectures. To alleviate this effect, optimization methods that consider qubit-to-qubit interactions in 2D grid architectures are presented in this paper. The proposed approaches benefit from Mixed Integer Programming (MIP) formulation for the qubit placement problem. Simulation results on various benchmarks show 27% on average reduction in communication overhead between qubits compared to best results of previous work.

Quantum algorithms for solving problems of interesting size often result in circuits with a very large number of qubits and quantum gates. Fortunately, these algorithms also tend to contain a small number of repetitively-used quantum kernels. Identifying the quantum logic blocks that implement such quantum kernels is critical to the complexity management for realizing the corresponding quantum circuit. Moreover, quantum computation requires some type of quantum error correction coding to combat decoherence, which in turn results in a large number of ancilla qubits in the circuit. Sharing the ancilla qubits among quantum operations (even though this sharing can increase the overall circuit latency) is important in order to curb the resource demand of the quantum algorithm. This paper presents a multi-core reconfigurable quantum processor architecture, called Requp, which supports a layered approach to mapping a quantum algorithm and ancilla sharing. More precisely, a scalable quantum mapper, called Squash, is introduced, which divides a given quantum circuit into a number of quantum kernels--each kernel comprises k parts such that each part will run on exactly one of k available cores. Experimental results demonstrate that Squash can handle large-scale quantum algorithms while providing an effective mechanism for sharing ancilla qubits.

Spin-Transfer Torque Magnetic RAM (STT-MRAM) technology requires a high current in order to write data into memory cells, which gives rise to large access transistors in conventional MOS-accessed cells. On the other hand, FinFET devices offer higher ON current and denser layout compared with planar CMOS transistors. This paper thus proposes the design of an energy-efficient STT-MRAM cell which utilizes a FinFET access transistor. To assess the performance of the new cell, optimal layout-related parameters of the FinFET access transistor and the MTJ are analytically derived in order to minimize the STT-MRAM cell area. Afterwards, detailed cell- and architecture-level comparisons between FinFET- vs. MOS-accessed STT-MRAMs are performed. According to the comparison results, while the area of the MOS-accessed STT-MRAM increases significantly under 3ns write pulse width (τw), the FinFET-based design can effectively function under τw= 2ns, at the cost of slight increase in the memory area. Hence, the FinFET-accessed STT-MRAM offers denser area and higher energy efficiency compared with the conventional MOS-accessed counterpart.

Improving circuit realization of known quantum algorithms by CAD techniques has benefits for quantum experimentalists. In this article, the problem of synthesizing a given function on a set of ancillea is addressed. The proposed approach benefits from extensive sharing of cofactors among cubes that appear on function outputs. Accordingly, it can be considered a multilevel logic optimization technique for reversible circuits. In particular, the suggested approach can efficiently implement any n -input, m -output lookup table (LUT) by a reversible circuit. This problem has interesting applications in the Shor's number-factoring algorithm and in quantum walk on sparse graphs. Simulation results reveal that the proposed cofactor-sharing synthesis algorithm has a significant impact on reducing the size of modular exponentiation circuits for Shor's quantum factoring algorithm, oracle circuits in quantum walk on sparse graphs, and the well-known MCNC benchmarks.

Optimization of the interaction distance between qubits to map a quantum circuit into one-dimensional quantum architectures is addressed. The problem is formulated as the Minimum Linear Arrangement (MinLA) problem. To achieve this, an interaction graph is constructed for a given circuit, and multiple instances of the MinLA problem for selected subcircuits of the initial circuit are formulated and solved. In addition, a lookahead technique is applied to improve the cost of the proposed solution which examines different subcircuit candidates. Experiments on quantum circuits for quantum Fourier transform and reversible benchmarks show the effectiveness of the approach.

Improving circuit realization of known quantum algorithms by CAD techniques has benefits for quantum experimentalists. In this paper, we address the problem of synthesizing a given k-input, m-output lookup table (LUT) by a reversible circuit. This problem has interesting applications in the famous Shor's number-factoring algorithm and in quantum walk on sparse graphs. For LUT synthesis, our approach targets the number of control lines in multiple-control Toffoli gates to reduce synthesis cost. To achieve this, we propose a multi-level optimization technique for reversible circuits to benefit from shared cofactors. To reuse output qubits and/or zero-initialized ancillae, we un-compute intermediate cofactors. Our experiments reveal that the proposed LUT synthesis has a significant impact on reducing the size of modular exponentiation circuits for Shor's quantum factoring algorithm, oracle circuits in quantum walk on sparse graphs, and the well-known MCNC benchmarks.

This paper proposes a novel all-optical router as a building block for a scalable wavelength-switched optical NoC. The proposed optical router, named as AOR, performs passive routing of optical data streams based on their wavelengths. Utilizing wavelength routing method, AOR eliminates the need for electrical resource reservation and the corresponding latency and area overheads. Taking advantage of Wavelength Division Multiplexing (WDM) technique, the proposed architecture is capable of data multicasting, concurrent with unicast data transmission, with high bandwidth and low power dissipation, without imposing noticeable area and latency overheads. Comparing AOR against previously proposed optical routers, we deduce that the proposed router architecture reduces optical insertion loss, electrical power consumption, and number of micro rings, and also improves scalability of the on-chip network.