An Optimization-Enhanced Low Power ALU Design using Clock Gating and Power Gating

Abstract

Low-power Arithmetic Logic Units (ALUs) form the computational backbone of modern energy-efficient digital systems, particularly in battery-powered and thermally constrained platforms such as Internet of Things (IoT) devices, mobile processors, and large-scale data centers. As semiconductor technologies continue to scale, power dissipation due to switching activity and leakage currents has emerged as a dominant limitation, often constraining performance, reliability, and system lifetime. Although conventional low-power techniques such as clock gating and power gating have shown promise, their static and heuristic-driven application is often insufficient to cope with dynamic workload variations and complex architectural interactions. This work presents an Optimization-Enhanced Low-Power ALU (O-ALU) that integrates metaheuristic-driven control with adaptive power management to achieve superior energy efficiency without compromising computational performance. The proposed architecture employs workload-aware activity monitoring combined with clock gating and power gating mechanisms that are dynamically optimized using intelligent search algorithms. This enables fine-grained, real-time adaptation of ALU sub-modules based on operational demand. The O-ALU is implemented and evaluated on an FPGA platform, and its performance is analyzed in terms of hardware resource utilization, dynamic and static power consumption, and execution efficiency. The experimental results demonstrate that the proposed design achieves significant reductions in both switching and leakage power while maintaining comparable throughput and latency to conventional ALU architectures. Moreover, the modular organization and adaptive control logic ensure scalability and robustness across varying workloads and technology nodes. Overall, the O-ALU provides a practical and effective solution for next-generation low-power processor design, offering a balanced trade-off between energy efficiency, performance, and architectural complexity.

Introduction

The text focuses on the design and optimization of low-power Arithmetic Logic Units (ALUs) to meet the growing demand for high performance and energy efficiency in modern embedded systems, portable devices, and high-performance computing platforms. As power consumption—driven by dynamic switching activity and static leakage currents—has become a major bottleneck in VLSI design, traditional ALU architectures are no longer sufficient. Consequently, advanced low-power techniques such as clock gating, power gating, operand isolation, dynamic voltage and frequency scaling (DVFS), and architectural restructuring are essential.

The ALU, as the core computational unit of a processor, significantly influences overall system performance and energy consumption. Conventional ALUs face challenges including high dynamic power, leakage at nanometer scales, thermal issues, and area constraints. Low-power ALUs are therefore critical for extending battery life in portable and IoT devices, reducing cooling and energy costs in data centers, and supporting sustainable computing.

The text outlines multi-level optimization strategies for ALU power reduction:

• Logic-level optimization reduces switching activity through Boolean simplification and glitch minimization.

• Circuit-level optimization focuses on leakage reduction using techniques such as transistor sizing, MTCMOS, and power-supply partitioning.

• Architectural-level optimization selectively activates functional units, shares resources, and adapts voltage and frequency to workload demands.

A proposed methodology integrates workload monitoring with an adaptive control framework, where a metaheuristic optimizer dynamically determines optimal clock and power gating policies. This allows inactive ALU sub-blocks to be disabled in real time, minimizing energy consumption while preserving performance.

Simulation and implementation results demonstrate the effectiveness of the approach. Clock gating significantly reduces dynamic power by disabling inactive registers and functional units, while power gating cuts leakage power using sleep transistors. VLSI power analysis shows that I/O and DSP blocks dominate power consumption, while logic resources consume relatively little power. Thermal analysis confirms safe operation, with junction temperatures well below critical limits.

Conclusion

Here we presented a comprehensive evaluation of the proposed Optimization-Enhanced Low-Power ALU (O-ALU) through detailed analysis of hardware utilization, power consumption, and performance metrics obtained from FPGA-based implementation. The objective was to verify whether the integration of metaheuristic-guided clock gating and power gating can effectively reduce energy consumption while maintaining reliable computational performance. The results clearly demonstrate that the proposed O-ALU meets these objectives. The resource utilization analysis shows that the O-ALU achieves an efficient and balanced use of Slice LUTs, registers, and DSP blocks. Although additional control and optimization logic is introduced to support adaptive power management, the overhead remains within acceptable limits. The modular structure of the arithmetic, control, and register units further enhances scalability and enables easy portability across different VLSI platforms and technology nodes. Power analysis confirms a substantial reduction in both dynamic and static power compared to conventional ALU designs. Dynamic power savings are mainly achieved through activity-aware clock gating, which suppresses unnecessary switching in idle or partially active sub-modules. Static power is reduced through power gating, which disconnects inactive functional units from the power supply, thereby minimizing leakage currents. The metaheuristic optimization framework plays a key role by continuously determining the optimal gating strategy based on real-time workload characteristics. Performance evaluation indicates that the O-ALU maintains comparable latency and throughput to traditional ALUs, despite the aggressive power-saving techniques employed. This shows that the trade-off between power efficiency and performance is effectively managed by the adaptive control framework. The design is therefore capable of supporting diverse workloads without compromising computational reliability. Overall, the results validate the proposed O-ALU as a scalable, adaptable, and energy-efficient arithmetic unit suitable for modern power-constrained applications. Its ability to deliver high performance under strict energy budgets makes it particularly attractive for IoT devices, portable electronics, and real-time embedded systems. The findings of this chapter strongly support optimization-driven power management as a promising approach for next-generation low-power processor design.

References

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Copyright

Copyright © 2026 Shivraj Singh Lodhi, Hariom Yadav, Ruhee Matolya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.