Memory Power Asymmetry (MPA)

• Memory Power Asymmetry (MPA) is a measure of systematic differences in power, energy, and memory resource requirements across various technologies and settings.
• Empirical studies show that MPA manifests through quantifiable metrics, such as lower energy per access in non-volatile memory and optimized write-efficient algorithm designs.
• MPA informs design strategies in hardware, algorithm development, and AI-human systems to balance resource demands and mitigate operational vulnerabilities.

Memory Power Asymmetry (MPA) refers to the systematic, often quantifiable, differences that arise in the power, energy, or memory resource requirements depending either on the type of memory technology, the directionality of memory-intensive processes, the roles of agents in dynamical systems, or the structure of social-technological relationships. MPA is observed across heterogeneous memory systems in computing hardware, in algorithmic models with non-volatile memory technologies, in agent-based learning systems with asymmetric memory capacity, in the theory of process transformations for classical vs quantum information systems, and in the emerging domain of AI–human relationships. This article synthesizes recent research to define, model, and characterize MPA in its various operational domains.

1. Formal Definitions and Quantitative Models

MPA can be defined precisely in several technical contexts:

• Hardware and Systems Context: For two memory types $A$ and $B$, Memory Power Asymmetry is given by

$$\mathrm{MPA}(A,B) = \frac{P_{\rm mem}(A)}{P_{\rm mem}(B)}$$

or equivalently by energy per access, $E_{\rm access}(A) / E_{\rm access}(B)$, where $P_{\rm mem}$ is average or instantaneous power under steady-state, memory-bound workloads (Proaño et al., 13 Aug 2024).

• Resource-aware Algorithms: In Asymmetric Random-Access Machine (ARAM) models, the cost of reads and writes can differ by a parameter $\omega\gg 1$, leading to cost functions of the form $C = R + \omega W$, for $R$ read-misses and $W$ write-backs, and all algorithmic resource analysis is made under explicit read/write power/energy asymmetry (Gu et al., 2018).
• Stochastic Process Transformations: For processes $P$ and $Q$, memory power asymmetry is expressed as

$$\Delta C_{\mu}(P, Q) = C_{\mu}[P \to Q] - C_{\mu}[Q \to P]$$

where $C_\mu[\cdot \to \cdot]$ is the minimal stationary-state Shannon (classical) or von Neumann (quantum) entropy required by a transformation device (finite-state $\varepsilon$-transducer) (Kechrimparis et al., 2023, Thompson et al., 2017).

• Human–AI Relationships: In social or organizational settings, MPA is conceptualized as a structural vector

$$\Delta M = M_{\rm ai} - M_{\rm h} = (P_{\rm ai} - P_{\rm h},\ A_{\rm ai} - A_{\rm h},\ X_{\rm ai} - X_{\rm h},\ I_{\rm ai} - I_{\rm h})$$

where the components denote persistence, accuracy, accessibility, and integration of memory with respect to the respective actors, typically favoring AI-enabled entities (Dorri et al., 7 Dec 2025).

Each context adopts unit conventions (Watts, Joules per access, entropy in bits, qualitative dimensions) suited to the domain, but all measure resource or power differentials traceable to underlying memory technology or capability.

2. Empirical Characterization and Mechanisms

2.1 Heterogeneous Memory Hardware

Empirical measurement of MPA is demonstrated in large-scale memory-bound workloads using dual-socket Cascade-Lake platforms supporting local DRAM (DDR4), Intel Optane DC Persistent Memory (NVM), and remote DRAM via NUMA. Average steady-state power under 18-thread, memory-pinned benchmarks showed:

• $P_{\rm DRAM} \approx 87$–$115$ W
• $P_{\rm NVM} \approx 73$–$78$ W
• $P_{\rm RemoteDRAM} \approx 78$–$85$ W with $\mathrm{MPA}(\text{DRAM},\text{NVM}) \approx 1.2$–$1.5$. NVM consistently delivered 10–25 W lower power draw than DRAM for given workloads (Proaño et al., 13 Aug 2024).

2.2 Embedded and Intermittent Systems

On TI MCUs (MSP430FR6989/5529), SRAM exhibits per-access energies $E_{\rm SRAM}^{rd} = 5.5\ \mu$J, $E_{\rm SRAM}^{wr} = 5.6\ \mu$J, while FRAM is $E_{\rm FRAM}^{rd} = 10.3\ \mu$J, $E_{\rm FRAM}^{wr} = 13.1\ \mu$J—an approximately 2$\times$ power and latency penalty for FRAM. Optimal code/data mapping via integer linear programming exploiting this asymmetry reduced system EDP by up to $38\%$ under stable power and up to $22\%$ under unstable power relative to prior schemes (Badri et al., 2023).

2.3 Algorithmic and Data Structure Regimes

Algorithmic consequences of MPA with $\omega\gg 1$ include:

• Write-efficient hash tables (multi-level): amortize inserts across $k$ tables, reducing write cost per insert to $1+2/(2^{k}-1)$ and boosting read costs by factor $k$.
• Balanced trees: BSTs (treaps) can minimize write frequency at the expense of additional read accesses.
• Cache-aware samplesort and rotating frontiers in BFS: trade more reads for far fewer writes, crucial under high $\omega$ (Gu et al., 2018).

3. Asymmetry in Learning and Multi-Agent Dynamics

Gradient reinforcement learning in zero-sum games with unequal agent memory—one agent possessing longer recall than its opponent—exhibits dynamical phenomena that directly instantiate MPA:

• Longer-memory agents can unilaterally exploit the concavity they induce in their opponent's effective utility, resulting in heteroclinic orbits in the learning dynamics.
• These orbits connect unstable to stable equilibria along the Nash manifold, leading to last-iterate convergence not found in symmetric-memory settings (Fujimoto et al., 2023).
• The role of asymmetric agent memory is essential: with equal memory, no such heteroclinic structure or induced convergence emerges.

4. Stochastic Process Transformation: Classical vs. Quantum

MPA also describes resource asymmetries in process transformation theory: transforming a stochastic process $P$ to $Q$ ("forward") or $Q$ to $P$ ("reverse") typically requires unequal memory resources classically:

• For complex families, $\Delta C_{\mu}(P,Q)$ can diverge with system size (e.g., $\sim\log n$ bits for coarse-graining families).
• Quantum transducers, however, can encode causal state ensembles far more compactly, potentially eliminating or even reversing the classical direction of MPA: in some transformations $C_q[P \to Q] < C_q[Q \to P]$ and $C_q \ll C_\mu$ even as $\Delta C_\mu \to \infty$ (Kechrimparis et al., 2023, Thompson et al., 2017).
• This quantum collapse of MPA discloses that the apparent "arrow" of process complexity is observer-dependent, linked to agent memory power (classical vs. quantum).

5. Social-Technological Dimensions: Human–AI Memory Power Asymmetry

In AI-mediated relationships, MPA emerges as a structural, multidimensional asymmetry in memory capability, formalized as a vector $\Delta M$ encompassing:

• Persistence: Indefinite AI retention vs. human forgetfulness.
• Accuracy: Bit-level logs vs. reconstructive, error-prone human memory.
• Accessibility: Indexable/queryable digital records vs. slow recall.
• Integration: Pattern extraction across massive relational histories vs. scattered, episodic human recall (Dorri et al., 7 Dec 2025).

These asymmetries translate to power by four mechanisms:

Mechanism	Key Dimensions	Example Operation
Strategic Memory Deployment	Accessibility, Integration	AI nudges based on historical patterns
Narrative Control	Persistence, Accuracy	AI's logs override human recollection
Dependence Asymmetry	Integration, Accessibility	User offloads "memory" to AI
Vulnerability Accumulation	Persistence, Integration	AI infers and exploits personal vulnerabilities

MPA, so defined, is distinct from information asymmetry or surveillance: it is specifically the asymmetric harnessing of shared relational history, not merely unequal data possession.

6. Design and Mitigation Strategies

Research proposes several architectural and policy-level interventions to mitigate harmful effects of MPA in human–AI relationships:

• Aligning AI memory use with articulated user goals.
• Transparency and contestability of retained/shared memory.
• Embedding forgetting and decay mechanisms in AI memory stores.
• Supporting human memory and agency rather than supplanting them.
• Contextual containment (limiting cross-domain memory sharing).
• Ecosystem-level oversight (retention regulation, portability rights, public accountability) (Dorri et al., 7 Dec 2025).

Analogous power-aware design principles apply in algorithmic and hardware domains: select memory technologies to balance power/latency trade-offs, partition workloads by memory affinity, and profile real-system workloads under variable allocation.

7. Broader Implications and Future Directions

MPA research unifies phenomena across physical systems, social-technical architectures, and computational learning via the principle that memory cost differentials drive operational, algorithmic, and relational outcomes. Essential theoretical insights include:

• MPA in physical computing is a driver for the development of write-efficient data structures and algorithms that exploit asymmetric energy budgets (Gu et al., 2018).
• Emergent phenomena in learning dynamics—such as induced concavity and heteroclinic connections to equilibrium—are structurally dependent on memory length asymmetry (Fujimoto et al., 2023).
• The invariance (or vanishing) of MPA in quantum process models suggests a new resource-theoretic understanding of complexity, with implications for the arrow of time and minimality under quantum information processing (Kechrimparis et al., 2023, Thompson et al., 2017).

In sum, Memory Power Asymmetry is a unifying cross-disciplinary construct: it quantifies, exploits, and, where needed, remediates the operational advantages and vulnerabilities that follow whenever memory power is unequally distributed across agents, technological systems, or social-technical relations.