Approximate Computing Circuit Design is a methodology intentionally relaxing computation accuracy to reduce power, area, and latency in applications tolerant of small errors — Approximate computing exploits inherent error tolerance in many applications including signal processing, multimedia, machine learning, and data analytics. Approximation Techniques include voltage scaling reducing power with timing errors, reduced-precision arithmetic lowering computational cost with quantization errors, and logic simplification removing error correction circuits. Voltage Scaling lowers supply voltage below normal operating points, accelerating errors but reducing quadratic power consumption, requiring error detection and recovery mechanisms. Approximate Operators include approximate adders with error injection, multipliers with reduced logic depths, and memory designs with probabilistic reads. Error Analysis characterizes error distributions through simulation, establishes error bounds for application requirements, and implements monitoring ensuring errors remain within acceptable ranges. Application Characterization identifies error-tolerant code regions including loops, approximate algorithms reducing strict correctness requirements. Quality Metrics measure computation quality through metrics application-specific (image SSIM, accuracy metrics) rather than binary correctness. Hardware Monitoring detects exceeded error thresholds through output validation, error detection codes, or probabilistic checking, triggering recovery mechanisms. Approximate Computing Circuit Design delivers energy efficiency through intelligent relaxation of computation accuracy requirements.