CacheFlow: Efficient Runtime Memory & Inference

• CacheFlow is a unified set of advanced techniques for efficient runtime memory handling, supporting live cache inspection in embedded systems, fast human motion prediction, and compressive video streaming for vision-language models.
• It leverages precomputation and caching methods—such as cached normalizing flows and dynamic token dropping—to reduce computational overhead and expedite inference.
• The framework’s modular designs address domain-specific challenges with significant performance gains across embedded introspection, motion forecasting, and scalable video analysis.

CacheFlow refers to a collection of advanced techniques and systems for efficient runtime memory handling, online content summarization, and fast generative inference in domains ranging from embedded caches and human motion modeling to streaming long-form video understanding. Three prominent frameworks labeled "CacheFlow" have appeared in recent literature: (1) live cache inspection for embedded systems (Tarapore et al., 2020), (2) fast human motion prediction via cached normalizing flows (Maeda et al., 19 May 2025), and (3) compressive streaming memory for long-form video understanding in vision-LLMs (Patel et al., 17 Nov 2025). Despite sharing a common goal of reducing runtime computational and memory overhead, these implementations target distinct infrastructure and algorithmic challenges.

1. Live Cache Inspection in Embedded Systems

CacheFlow, as detailed by Mai et al., is the first software-only solution for live cache content snapshotting on ARM-based embedded platforms (Tarapore et al., 2020). Leveraging vendor-exposed cache introspection interfaces (specifically, the ARMv8 RAMINDEX registers), CacheFlow acquires the complete tag store and content of last-level caches (LLCs) on live Linux systems, obviating the need for external hardware debuggers.

The system is architected with two core modules:

• Trigger (user-space daemon): Responsible for generating events (periodic, signal- or breakpoint-driven) and orchestrating synchronous or asynchronous pause/resume control over observed tasks.
• Shutter (Linux kernel module): Executes an IPI-based inter-core locking protocol to ensure quiescence, then iterates through all cache ways and sets, reading valid tags via RAMINDEX and exporting them to a non-cacheable buffer.

Data postprocessing includes optional reverse physical-to-virtual mapping using Linux's rmap_walk, enabling per-process cache line attribution.

2. Cached Normalizing Flow for Fast Human Motion Prediction

CacheFlow for human motion prediction implements a two-stage stochastic generative modeling pipeline that decouples the expensive normalizing flow computation from conditional inference (Maeda et al., 19 May 2025).

• Stage 1 (Unconditional Flow Training and Caching): An unconditional normalizing flow $$f_\theta : \mathbb{R}^d_z \rightarrow \mathbb{R}^d_x$$ is trained on future motion latents. For a dataset $\{x_k\}$, their preimages $z_k = f^{-1}_\theta(x_k)$ and Jacobian terms $|\det \partial f_\theta / \partial z|_{z_k}^{-1}$ are computed and cached.
• Stage 2 (Conditional Lightweight Mapping): A small conditional model $\phi$ predicts a Gaussian mixture density $q_\phi(z|c)$ from current context $c$. The likelihoods $p(x_k|c) = q_\phi(z_k|c) \cdot \Delta_k$ are calculated using only cached flow outputs and the fast conditional model.

This strategy yields a 1 ms inference pipeline—30× faster than state-of-the-art diffusion models—without loss in accuracy or density estimation capability, as shown in Human3.6M and AMASS benchmarks.

3. Compressive Streaming Memory for Long-Form Video Understanding

The CacheFlow system for vision-LLMs (VLMs) addresses the exponential growth of key-value (KV) caches during video question answering (VQA) (Patel et al., 17 Nov 2025). It is a training-free, drop-in module designed for live, streaming, or offline long-form video analysis, centering on three core mechanisms:

• Dynamic Token Dropping (DTD): At each incoming frame $t$, per-patch feature similarity to the previous frame is computed using cosine distance. Tokens exceeding a similarity threshold $\tau_\mathrm{feat}$ are dropped, resulting in 70–87% token reduction.
• Block Packing and Compressive Memory: Surviving tokens are grouped into fixed-size blocks. When evicted from the local GPU window, KV caches for each block are offloaded, and a frozen GRU summarizes each block into a low-dimensional vector stored as a retrieval key. All full block KV pairs remain available for lossless "rehydration."
• Consensus-Based Retrieval: At question time, query vectors from shallow and deep Transformer layers are used to retrieve top-K relevant blocks via layer-wise cosine similarity. Only the selected blocks are loaded and attended over, significantly reducing quadratic attention and memory bottlenecks.

CacheFlow achieves up to 87% reduction in tokens and substantial runtime and memory savings, outperforming strong sliding-window and recurrent cache baselines in accuracy and answer quality on QAEgo4D, MLVU, EgoSchema, and streaming RVS-Ego/RVS-Movie benchmarks.

4. Key Algorithms and Mathematical Formulations

Each CacheFlow instantiation relies on problem-adaptive algorithmic design:

• Embedded Inspection: The kernel module iterates over all sets and ways, writes to RAMINDEX, reads back DL1DATA registers, and stores valid lines. Physical to virtual mapping uses rmap_walk. For snapshot complexity, $O(W \cdot S)$ RAMINDEX operations are required per capture.
• Motion Prediction: The use of the change-of-variable formula in normalizing flows, $$p_X(x) = p_Z(f_\theta^{-1}(x)) \cdot |\det \partial f_\theta^{-1} / \partial x|$$, is complemented by cached computation of preimages and Jacobians. The conditional density $p(x_k|c)$ requires only evaluating $q_\phi(z_k|c)$ and multiplying by cached determinants.
• Video Memory: DTD logic compares patch-wise feature vectors $x_{t,p}$ and $x_{t-1,p}$ via cosine similarity $s_{t,p}$, enforces per-frame keep/drop masking, and streams survivors into blocks. Compression applies a single-layer GRU, with information retrieval scoring driven by consensus of layer-0 and top-layer query–memory vector matches.

5. Experimental Evaluations and Results

All three CacheFlow lines undergo rigorous empirical testing on real hardware or representative benchmarks:

• Embedded Systems: On NVIDIA Tegra TX1, full-flush snapshotting yields $\sim$95% cache line pollution and $\sim$5.3 ms overhead, while transparent mode achieves $\sim$1% pollution and $0.1$ ms overhead. Synthetic and real application traces (SD-VBS) substantiate CacheFlow's ability to resolve per-phase and cross-process cache dynamics, predict slowdowns, and infer random replacement policy behavior.
• Human Motion: CacheFlow infers 50 future samples in 1.3 ms—a $4\times$ speedup over the fastest VAE and $30\times$ faster than diffusion baselines. Best-of-N and log-likelihood scores meet or exceed state-of-the-art. Removal of unconditional flow or precomputed cache degrades accuracy and increases runtime ($0.369 \to 0.616$ ADE, $1.3 \to 415.9$ ms).
• Video QA: On QAEgo4D and EgoSchema, CacheFlow with DTD and GRU achieves 2–3 point accuracy gains over ReKV baselines while discarding large fractions of tokens (up to 87%). Latency and GPU memory consumption reductions range from $1.6 \to 1.38$ s (0.5B) and $3.3 \to 2.03$ s (7B), with memory down $19 \to 2.9$ GB and $38 \to 26.4$ GB, respectively.

6. Limitations and Future Work

Each CacheFlow system highlights context-specific limitations:

• Embedded: Current implementation is ARMv8-specific, requires PIPT shared L2; generalization to other architectures, OS kernel or hypervisor integration, and extension to L1/branch predictor/TLB inspection are proposed avenues (Tarapore et al., 2020).
• Motion Prediction: The expressiveness bottleneck is at the mapping from context $c$ to $q_\phi$ and the finite size of the cached training set; online caching or hybridization with dynamic flows could further balance speed and flexibility (Maeda et al., 19 May 2025).
• Video Memory: A plausible implication is that DTD may omit rare but contextually crucial frames; the GRU-based summarizer, while superior to mean-pooling, could be further refined, and index compaction may become increasingly important for longer sequences. Extension to active or learned DTD policies is suggested (Patel et al., 17 Nov 2025).

7. Impact Across Domains

CacheFlow frameworks directly address the central challenge of memory and compute bottlenecks in runtime-intensive tasks—enabling, with rigorous formal treatment and ablation, live system introspection, accelerated probabilistic generative modeling, and scalable context-aware processing for very long sequence tasks. The term "CacheFlow" now denotes a paradigm of strategic precomputation, online compression, and selective on-demand memory access, as evidenced by diverse applications in embedded introspection, human motion prediction, and vision-language video understanding (Tarapore et al., 2020, Maeda et al., 19 May 2025, Patel et al., 17 Nov 2025).