SearcharxivSearch

arXiv · 1805.04074

Hybrid CMOS-CNFET based NP dynamic Carry Look Ahead Adder

Abstract

Advanced electronic device technologies require a faster operation and smaller average power consumption, which are the most important parameters in very large scale integrated circuit design. The conventional Complementary Metal-Oxide Semiconductor (CMOS) technology is limited by the threshold voltage and subthreshold leakage problems in scaling of devices. This leads to failure in adapting it to sub-micron and nanotechnologies. The carbon nanotube (CNT) technology overcomes the threshold voltage and subthreshold leakage problems despite reduction in size. The CNT based technology develops the most promising devices among emerging technologies because it has most of the desired features. Carbon Nanotube Field Effect Transistors (CNFETs) are the novel devices that are expected to sustain the transistor scalability while increasing its performance. Recently, there have been tremendous advances in CNT technology for nanoelectronics applications. CNFETs avoid most of the fundamental limitations and offer several advantages compared to silicon-based technology. Though CNT evolves as a better option to overcome some of the bulk CMOS problems, the CNT itself still immersed with setbacks. The fabrication of carbon nanotube at very large digital circuits on a single substrate is difficult to achieve. Therefore, a hybrid NP dynamic Carry Look Ahead Adder (CLA) is designed using p-CNFET and n-MOS transistors. Here, the performance of CLA is evaluated in 8-bit, 16-bit, 32-bit and 64-bit stages with the following four different implementations: silicon MOSFET (Si-MOSFET) domino logic, Si-MOSFET NP dynamic CMOS, carbon nanotube MOSFET (CN-MOSFET) domino logic, and CN-MOSFET NP dynamic CMOS. Finally, a Hybrid CMOS-CNFET based 64-bit NP dynamic CLA is evaluated based on HSPICE simulation in 32nm technology, which effectively suppresses power dissipation without an increase in propagation delay.

Explore related subjects

Keep this discovery

BibTeXRIS

A. Nagalakshmi, Ch. Sirisha, Dr. D. N. Madhusudana Rao. 2018-05-10. Hybrid CMOS-CNFET based NP dynamic Carry Look Ahead Adder. https://arxiv.org/abs/1805.04074

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

From Grid to Chip: Power Architecture, Stability, and Flexibility of AI Data Centers

The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads. Their deployment is primarily constrained by energy availability and grid-connection capacity, which is further aggravated by the ability of power-delivery architectures, control systems, and computing workloads to operate reliably during fast grid disturbances. This article presents a technological perspective on AI data centers as grid-interactive computing systems. First, it reviews grid-integration bottlenecks, evolving connection policies, grid-code requirements, which has fostered new technological trends via spatio-temporal flexibility available through workload orchestration, cooling systems, on-site resources, and energy storage. Second, it maps the evolution of power-delivery architectures from medium-voltage grid interfaces to chip-level, discussing higher-voltage DC distribution, solid-state transformers, wide-bandgap devices, advanced chip-level power delivery, and liquid cooling. Third, it establishes a three-level stability framework spanning rack-level DC-bus dynamics, facility-level converter interactions, and system-level grid-coupled behavior. The framework connects dominant instability mechanisms, including constant power load effects, impedance interactions, forced oscillations, and operating-mode transitions, with suitable modeling, assessment, and mitigation approaches. Synthesizing these topics, this article highlights grid-to-chip co-design as a central requirement for scalable AI infrastructure, linking computing workloads, power-delivery systems, energy buffers, and grid operation.

cs.ET

A Time-Based Readout for Vector-Matrix Multiplication in Fully Analog Memristive SNNs

Artificial neural networks rely on vector-matrix multiplications (VMMs), whose implementation in von Neumann architectures is dominated by costly data movement between memory and processing units. Spiking neural networks (SNNs) mitigate this bottleneck by performing in-memory, analog VMMs using memristive crossbar arrays. However, conventional current-mode readout circuits incur significant area and power overhead. This work proposes a fully analog readout architecture based on voltage-to-time conversion of the VMM output. By sensing the column voltage, the proposed approach avoids current-mode summing and scaling circuitry, improving area and energy efficiency. Post-layout simulations of a 10x1 SNN implemented in a 130 nm CMOS technology validate the proposed architecture, while application to a trained 64x10 SNN for digit classification further demonstrates its feasibility for SNN inference.

cs.ET

Fractional-order hardware for neuromorphic computing: Is the order really the problem?

Does a neuromorphic system need a true power-law memory kernel, and if so, can anyone build one? Neuromorphic systems process signals spanning many timescales at once, from milliseconds to tens of seconds. Integer-order circuits buy each additional timescale with an additional state variable. Fractional-order dynamics offer a different bargain: one operator whose power-law kernel carries a continuum of timescales, tuned by one parameter, the order alpha. A fractional derivative is non-local, so evaluating it costs storage and arithmetic that grow with the retained history, where an integer-order derivative costs a constant. This review organizes the hardware literature around that cost. We derive the retained history needed to hold the truncation error below a tolerance epsilon, show that it scales as epsilon^(-1/alpha), and set beside it a second and independent limit on the direct form: in fixed point the weights themselves underflow, so word length caps the usable history however long the buffer is. The two limits move at very different rates with the order, and where they cross decides whether a word length can serve an order at all. We use both to sort published hardware into three strategies, note a fourth the numerical literature has developed and this hardware has not, and survey digital, analog and device work. Along the way we ask whether the field is worried about the right obstacle. It is not. Fabricated constant-phase devices already span the orders two groups identify as task-optimal, so the order gap has largely closed, leaving a residual gap near 0.1 and at the lower order describing cortical adaptation. What remains is a frequency-band gap of about three decades at the low end. That corner is not empty, since double-layer electrodes work there, but every device in it is discrete, and no integrable thin-film element has been characterized there.

cs.ET