Performance Improvement Overview

Principles

Definition

Performance improvement is a systematic methodology applied to identify, analyze, and enhance the effectiveness and efficiency of processes, systems, or human performance within a defined context. This field of study investigates principles and techniques for optimizing outcomes through targeted interventions and continuous evaluation.

Ontological type

Core Methods

Measurement and Evaluation

Needs Assessment

Key developments

Key Figures

Integrated Performance Systems era

Andrew Y. Ng [1] is a leading figure in the Integrated Performance Systems era, with affiliations at Stanford University [3] and the University of California, Berkeley [4]. His key contributions include advancing empirical evaluation and benchmarking to balance speed, cost, and quality, as reflected in the paper Cheap and fast---but is it good? [7], which anchored performance improvements in this era. Liang Shen [2] is a prominent contributor in this era, with affiliations at Jilin University [5] and Tsinghua University [6]. His paper Performance improvement of inverted polymer solar cells with different top electrodes by introducing a MoO3 buffer layer [8] demonstrates material-level optimization strategies that deliver measurable performance gains, illustrating how engineering the interface and buffer layers underpins cross-domain performance improvements in integrated systems.

Energy-Aware Perception Hardware era

Anantha P. Chandrakasan [1] is a leading figure associated with Massachusetts Institute of Technology [3] and the University of California, Berkeley [4] during the Energy-Aware Perception Hardware era. His key contributions in this era include the design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors [7], a development that reduced energy and bandwidth demands for sensing systems. Liyuan Han [2] is associated with Huazhong Agricultural University [5] and the University of Science and Technology of China [6] during this era. Her key contributions include the Hybrid interfacial layer leads to solid performance improvement of inverted perovskite solar cells [8], a finding that demonstrates interfacial engineering to boost energy conversion efficiency and stability for energy-aware sensing and power platforms.

Edge AI Co-Design era

William J. Dally [1], associated with MIT [3] and Stanford University [4] during the Edge AI Co-Design era, helped shape energy-aware hardware-software co-design for edge inference. In the EIE paper [6], Dally advanced an energy-efficient inference approach that exploits sparsity and minimizes data movement, addressing the low-latency, resource-constrained needs of edge AI. Mark Horowitz [2], affiliated with Stanford University [4] and Seoul National University [5], contributed to the era's co-design discourse. Horowitz's work on EIE [6] further illustrates hardware-aware inference that enables practical, energy-efficient edge deployments, uniting algorithms and circuitry in the era.