EnergAIzer: Energy-Aware Systems

• EnergAIzer is defined as two distinct frameworks: one for GPU power estimation in AI workloads and another for aging-aware residential energy management.
• The GPU framework achieves about 8% power error and 1.8-second estimation time by modeling kernel optimization structures through analytical and empirical fitting.
• The residential framework optimizes day-ahead energy management by balancing grid cost, battery degradation, and EV state-of-charge using physics-based models.

EnergAIzer is a name used in recent arXiv literature for at least two distinct energy-aware research systems with different technical scopes. In one usage, it denotes a fast GPU power estimation framework for AI workloads that replaces costly simulation or hardware profiling of utilization inputs with a lightweight performance model, achieving end-to-end prediction in about 1.8 seconds per workload and about 8% power error on NVIDIA Ampere GPUs (Lee et al., 22 Apr 2026). In another usage, it denotes an aging-aware day-ahead energy management framework for residential multi-carrier energy systems that coordinates electricity, heat, and mobility assets while explicitly optimizing the trade-off between grid cost and battery degradation through physics-based battery aging models (Slaifstein et al., 20 Mar 2025). The shared name therefore identifies a broader research motif: explicit energy-aware decision support, but instantiated in substantially different domains.

1. Name, scope, and disambiguation

The term EnergAIzer appears in at least two separate arXiv papers with different problem formulations, state variables, and objectives. The 2026 paper "EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads" presents a framework for estimating dynamic GPU power consumption for AI kernels by predicting hardware utilization analytically and empirically rather than obtaining it from cycle-level GPU simulation or hardware profiling (Lee et al., 22 Apr 2026). The 2025 paper "Aging-aware Energy Management for Residential Multi-Carrier Energy Systems" uses EnergAIzer to denote an aging-aware day-ahead energy management framework for electrified residential buildings, with explicit coupling among electricity, heat, and mobility assets and explicit accounting for battery degradation (Slaifstein et al., 20 Mar 2025).

The two systems share an emphasis on exposing and optimizing energy-relevant structure rather than treating energy as a secondary proxy. In the GPU setting, the central bottleneck is obtaining utilization inputs such as DRAM activity, Tensor Core usage, cache traffic, or SM occupancy quickly enough for design-space exploration (Lee et al., 22 Apr 2026). In the residential MCES setting, the central bottleneck is representing battery degradation with sufficient physical fidelity that the scheduler can make meaningful trade-offs between immediate operating cost and lifetime wear (Slaifstein et al., 20 Mar 2025). This suggests that the commonality lies less in a shared architecture than in a shared methodological stance: energy is treated as a first-class optimization target.

2. EnergAIzer for AI-workload GPU power estimation

The GPU-oriented EnergAIzer is motivated by the observation that AI workloads are rapidly increasing datacenter power consumption and that GPUs are the main power consumers in modern AI systems (Lee et al., 22 Apr 2026). Existing power models can be accurate, but they typically require hardware utilization inputs that are conventionally obtained either by cycle-level GPU simulation, which can take hours, or by hardware profiling such as Nsight Compute, which still takes minutes and requires execution on a real GPU (Lee et al., 22 Apr 2026). The framework addresses this scalability bottleneck by replacing expensive utilization acquisition with a lightweight model that predicts utilization analytically and empirically.

Its key insight is that dominant AI kernels are not arbitrary programs; they usually follow structured optimization patterns such as tiling, threadblock scheduling/swizzling, pipelining, and double buffering (Lee et al., 22 Apr 2026). Those patterns determine memory traffic across DRAM, L2, and shared memory, distribute work across SMs and SMSPs, and determine latency hiding and overlap. EnergAIzer therefore models the optimization structure that organizes instructions rather than modeling raw instructions directly (Lee et al., 22 Apr 2026).

The framework proceeds in three stages. First, it represents the kernel using tensor shapes plus optimization parameters. Second, it builds a performance model using the structured execution pattern as an analytical scaffold and refines it with empirical fitting. Third, it feeds the predicted module-level utilization into a power model to estimate dynamic power (Lee et al., 22 Apr 2026). The targeted kernel classes include GEMM-like matrix multiplication, reductions like softmax, elementwise kernels, and fused kernels like FlashAttention (Lee et al., 22 Apr 2026).

A central technical distinction is that EnergAIzer predicts module-level utilization for DRAM, L2 cache, shared memory, Tensor Cores, CUDA Cores, and Special Function Units (Lee et al., 22 Apr 2026). Prior lightweight performance models are described as too coarse for this purpose because latency depends on the slowest path, whereas power depends on how much of each module is active on average. The framework therefore derives utilization from a coarse-grained execution timeline consisting of actions such as G→S, S→R, R→S, S→G, and compute steps on Tensor Cores, CUDA Cores, or SFUs (Lee et al., 22 Apr 2026).

3. Analytical scaffold, power model, and reported results

The performance model uses module-based action latencies. The paper states the first-order ideal latency for an action as

$$t_\text{action}^\text{module} = \frac{\text{amount of work}}{\text{bandwidth}/\text{concurrency}},$$

with the realized action latency determined by the slowest participating module; for example,

$$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$

Because large kernels are modeled more accurately than small kernels, EnergAIzer adds an empirical correction,

$$\hat{t}_\text{corrected} = \lambda \times t_\text{ideal} + \varepsilon,$$

and, for GEMM, phase-specific correction terms for prologue, mainloop, and epilogue (Lee et al., 22 Apr 2026). The coefficients are fitted offline by solving a quadratic optimization problem over grouped kernels with similar optimization structure.

Module utilization is then extracted from corrected timelines via

$$\alpha_\text{module} = \frac{\text{active time of module}}{\text{total kernel latency}} .$$

For device-wide prediction, the model averages utilization across busy SMs and lazy SMs, reflecting the claim that power depends on average utilization rather than only on the critical-path SM (Lee et al., 22 Apr 2026). The resulting utilization terms are inserted into a dynamic power model of the form

$$P^\text{dyn} = \alpha_\text{DRAM} \cdot C_\text{D}V_\text{D}^2f_\text{D} + \sum_{\text{modules}} \alpha_\text{module}\cdot C_\text{m} V^2f,$$

with total power obtained by adding idle power measured separately (Lee et al., 22 Apr 2026).

Reported quantitative results are a major part of the system’s significance. On NVIDIA Ampere GPUs, EnergAIzer achieves 8.0–8.2% average power error and 8.8–11.0% average latency error, while kernel-level power error for GEMM, Softmax, and FlashAttention at representative frequency is roughly 3.1–3.8% MAPE (Lee et al., 22 Apr 2026). End-to-end estimation takes about 1.8 seconds per workload, versus 0.4–1.5 hours for simulation and 8.5–35 minutes for Nsight Compute profiling, corresponding to speedups of roughly 317× to 3856× (Lee et al., 22 Apr 2026). The framework also supports frequency scaling and architectural exploration, reporting about 6–9% MAPE across 510–1410 MHz on A100 and forecasting NVIDIA H100 with 7% power error, L40S with 13% error, and A100-80GB-SXM with 9.1% error using A100-40GB-PCIE training data (Lee et al., 22 Apr 2026).

The paper interprets these results as enabling power-aware compiler and kernel tuning, datacenter resource allocation, hardware selection, early architectural exploration, and pre-deployment energy forecasting (Lee et al., 22 Apr 2026). A plausible implication is that the framework’s principal contribution is not a new CMOS power equation, but a new route to obtaining the activity factors $\alpha$ quickly enough to make iterative exploration practical.

4. EnergAIzer for residential multi-carrier energy systems

The residential MCES EnergAIzer addresses a different problem: energy management for an electrified home that couples electricity, heat, and mobility assets while explicitly accounting for battery aging (Slaifstein et al., 20 Mar 2025). The considered system includes PV, BESS, EV charging, grid connection, and electric load on the electricity side; solar thermal, heat pump, thermal energy storage system, and thermal load on the heat side; and an EV with departure/arrival availability constraints on the mobility side (Slaifstein et al., 20 Mar 2025). The challenge is to satisfy user requirements and device constraints under uncertain forecasts for PV, loads, EV availability, and prices, while balancing grid costs against storage degradation.

The framework formulates the problem as a sequential decision problem but solves it with a deterministic day-ahead lookahead planner using forecast medians (Slaifstein et al., 20 Mar 2025). The objective combines grid cost, battery degradation cost, and an EV departure SoC penalty: $$\min_{P^*_{a,t}} \mathbb{E}_W\left[C_{\textrm{grid}} + C_{\textrm{loss}} + p_{\textrm{SoCDep}}\right].$$ The grid-cost term uses buying and selling electricity prices and grid import/export, while the degradation term uses the unit degradation cost, cell series/parallel counts, and the capacity-fade current $i_{\text{loss},as,t}$; the SoC penalty enforces the requested EV state of charge at departure (Slaifstein et al., 20 Mar 2025). The stated interpretation is explicit: the EMS trades off lower grid cost through more aggressive battery cycling and arbitrage against lower battery degradation through gentler operation that avoids harmful SoC and C-rate regions (Slaifstein et al., 20 Mar 2025).

Operationally, the method is a Direct Lookahead (DLA) day-ahead policy: $$X_t^\pi(S_{a,t}) = \arg\min_{P_{a,t},\ldots,P_{a,t+H}} \tilde{C}_{\textrm{grid}} + \tilde{C}_{\textrm{loss}} + \tilde{p}_{\textrm{SoCDep}} + \tilde{p}_{\textrm{TESS}},$$ subject to approximate dynamics $\tilde{S}^M_a$ (Slaifstein et al., 20 Mar 2025). The planner uses approximated models, while a high-fidelity simulator recomputes grid and TESS balancing using actual exogenous inputs, simulates battery performance with LiiBRA.jl / PBROM, simulates degradation with the physics-based aging model, rejects infeasible actions that violate bounds, and feeds the corrected state back to the optimizer (Slaifstein et al., 20 Mar 2025). The resulting architecture is therefore a planning + simulation loop rather than a standalone optimizer.

5. Battery performance, aging physics, and empirical findings

A defining characteristic of this EnergAIzer is its separation of battery performance model $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$0 and aging model $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$1, with state and parameter evolution given by

$$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$2

and

$$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$3

Two performance models are used: a simple bucket model and a first-order equivalent circuit model (ECM) that incorporates diffusion and internal resistance (Slaifstein et al., 20 Mar 2025). For aging, the paper compares an empirical aging model based on Wang et al. with a physics-based reduced-order model (PBROM) based on Jin et al. (Slaifstein et al., 20 Mar 2025).

The PBROM contains two dominant degradation mechanisms: SEI growth and active material loss (AM), with

$$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$4

The SEI term depends on electrochemical overpotential, Li stoichiometry, anode OCV, and temperature, while the AM term captures mechanical-stress-induced active material degradation (Slaifstein et al., 20 Mar 2025). The paper also models growth in internal resistance $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$5 through SEI thickness growth, but explicitly notes that $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$6 is not directly optimized in the planner because doing so would add nonconvexity; instead it is updated in the simulator (Slaifstein et al., 20 Mar 2025).

The framework is also chemistry-aware but not chemistry-restricted. The same physics-based aging equations are used for both NMC and LFP cells, with differences captured through parameter values rather than through a different empirical form (Slaifstein et al., 20 Mar 2025). The stated rationale is that both chemistries use graphite anodes, while cathode and electrolyte differences are reflected in the parameter set (Slaifstein et al., 20 Mar 2025). This suggests a design preference for transferable physical structure over chemistry-specific empirical fitting.

Three benchmark planners are compared in case study I: BNoDeg (bucket model, no degradation), CEmpDeg (ECM + empirical aging), and CPBDeg (ECM + physics-based aging) (Slaifstein et al., 20 Mar 2025). The paper reports that CPBDeg achieved the lowest battery degradation in both summer and winter, generally accepting a higher grid cost than the cheapest benchmark because it avoided harmful cycling (Slaifstein et al., 20 Mar 2025). In summer, the table gives grid cost values of 16.01 € for BNoDeg, 15.22 € for CEmpDeg, and 20.28 € for CPBDeg, while capacity fade is 206.9 mAh, 216.4 mAh, and 206.6 mAh, respectively (Slaifstein et al., 20 Mar 2025). BNoDeg is reported to have roughly 45% rejected actions in summer due to model mismatch (Slaifstein et al., 20 Mar 2025).

In case study II, identical pack ratings with different chemistries show that LFP yields 25% lower grid cost in summer and about 25% lower capacity fade compared to NMC (Slaifstein et al., 20 Mar 2025). In case study III, the planner is evaluated on fresh NMC batteries and aged NMC batteries at SoH = 90%, represented by a 5% increase in $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$7 and a 10% decrease in available lithium content $$t_{G\to S}=\max(t^{DRAM}_{G\to S}, t^{L2}_{G\to S}, t^{shared}_{G\to S}) .$$8 (Slaifstein et al., 20 Mar 2025). The reported finding is that aging-aware control becomes more valuable as the battery degrades, with CPBDeg maintaining lower capacity fade than BNoDeg across seasons and health states (Slaifstein et al., 20 Mar 2025).

6. Position in the broader energy-aware systems literature

The two EnergAIzer systems occupy different points in a broader energy-aware design space that also includes model-driven mobile software, embedded RL energy managers, batteryless IoT platforms, and inference-serving energy models. In mobile software, eGEN is a textual DSML plus a code generator for self-adaptive, battery-aware location sensing in native Android apps; it supports policy specification at design time and reports 4.35 minutes average GPS reduction per hour, 188 mA average reduction in battery consumption, and only 97 meters average degradation in location accuracy over a 3,060 m cycling path across five open-source Android apps (Boyalakuntla et al., 2022). In resource-constrained wearables, tinyMAN is a prediction-free RL-based manager for energy-harvesting devices using PPO, deployed with TensorFlow Lite for Micro at less than 100 KB memory footprint and reporting inference overheads as low as 0.75 ms, 6.75 µJ and up to 45% higher utility than prior approaches (Basaklar et al., 2022).

For batteryless IoT, InfiniteEn couples a multi-source energy combiner with a Load Monitoring Module (LMM), reporting 88% nominal efficiency and response time less than 11 ms to energy-state changes while remaining load-agnostic (Puluckul et al., 2024). At the distributed network level, the framework in "Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks" combines ATEM and VSDA, using an LSTM model for harvester-power forecasting and reporting at least 98% less data loss, at least 100 ms less packet delay, and at least 15.28% higher hardware component availability than baselines (Singhal et al., 2023). In renewable energy communities, the similarly named EnergAIze applies MADDPG to V2G management, reporting REC-level reductions of -20.80% in average daily peak and -35.22% in ramping while allowing each prosumer to choose personal objectives such as cost minimization, self-consumption maximization, or carbon-emission reduction (Fonseca et al., 2024).

Within AI-system energy modeling, EnergyLens provides an interpretable contrast to the GPU-oriented EnergAIzer. EnergyLens uses symbolic regression to derive a single twelve-parameter closed-form energy model for multimodal LLM inference serving, fitted from about 50 profiling measurements, and reports 88.2% Top-1 configuration selection accuracy across 940 total scenarios, with the additional claim that latency and energy optima diverge in more than 20% of tested configurations and can incur up to 79.8% energy penalty when latency is used as a proxy (Palladino et al., 11 May 2026). Relative to that work, the GPU-oriented EnergAIzer is centered on module-level utilization inference for kernel power prediction, whereas EnergyLens is centered on closed-form serving-configuration selection for inference deployment (Lee et al., 22 Apr 2026, Palladino et al., 11 May 2026).

Taken together, these systems show that the name EnergAIzer sits within a family of research efforts that encode energy-relevant structure directly into modeling, planning, or control. In the GPU case, the structure is kernel tiling, pipelining, and module activity (Lee et al., 22 Apr 2026). In the residential MCES case, it is battery electrochemistry, day-ahead lookahead, and multi-carrier coupling (Slaifstein et al., 20 Mar 2025). A plausible implication is that the recurring research pattern behind the name is not domain specificity, but the attempt to make energy optimization both explicit and operationally usable.