Indirect Memory Acceleration

• Indirect Memory Acceleration is a paradigm that decouples logical memory access from physical data movement through an intermediate indirection layer.
• Techniques such as pointer-based access, cache offloading, and in-memory copying enable improved bandwidth utilization and reduced latency.
• Empirical studies demonstrate significant performance gains, including multi-fold speedup and energy efficiency improvements across various application domains.

Indirect memory acceleration refers to a class of architectural, algorithmic, and protocol-level techniques that enhance computational throughput, latency, or energy efficiency not by directly increasing memory bandwidth or shrinking working sets, but by interposing a layer of indirection between memory access requests and underlying data movement or computation. Indirection in this context means transparently modifying where, when, or how data is fetched, placed, or processed relative to the computation, often by leveraging redundancy, data layout, data path compression, memory-side compute, or spatio-temporal reuse. Indirect memory acceleration explicitly encompasses both hardware and software mechanisms, including but not limited to near-memory coalescing, compression, selective offloading, pointer-based access, in-memory commands, memory-aware protocol adaptation, and multi-modal control strategies, as exemplified across video diffusion pipelines (Xiao et al., 6 Oct 2025), genomics accelerators (Lim et al., 23 Feb 2025), band-limited DRAM copies (Seshadri et al., 2018), gather/scatter in vector systems (Zhang et al., 2023), and algorithmic memory-aware scheduling in stochastic dynamics (Ibáñez et al., 13 Mar 2026).

1. Architectural and Algorithmic Principles

The central architectural insight in indirect memory acceleration is that decoupling logical data placement, access, and processing from the underlying physical realization allows systems to selectively exploit locality, redundancy, or transform inefficiencies that would otherwise be intractable at the direct memory interface. This decoupling is frequently instantiated by:

• Hardware- or software-managed caches for intermediate state or redundant representations, but swapped or staged off-band (e.g., CPU-pinned memory rather than device memory in LightCache (Xiao et al., 6 Oct 2025)).
• Indirection buffers or pointer-based access strategies, as in the replacement of im2col with pointer arrays in deep learning convolutional kernels (Dukhan, 2019).
• In-DRAM execution and row-level copying to bypass the memory channel, as in RowClone (Seshadri et al., 2018).
• Near-memory or co-processor units that reorder, coalesce, or compress memory access streams before reaching aggressively band-limited DRAM or high-bandwidth memory (HBM) resources (Zhang et al., 2023, Khadem et al., 29 May 2025, Lim et al., 23 Feb 2025).
• Memory-aware, multi-mode scheduling that leverages system memory effects, such as multi-exponential temporal relaxations in stochastic or physical systems (Ibáñez et al., 13 Mar 2026).

In each approach, the common mechanism is the relocation or manipulation of address or data movement “indirection” away from the primary compute-data loop, often enabling reuse, effective bandwidth improvement, logical elimination of bottlenecks, or the circumvention of artificial memory fencing or serialization costs.

2. Representative Implementation Strategies

Multiple concrete strategies instantiate the indirect memory acceleration concept:

1. Pointer-Indirection and Indirection Buffers: The Indirect Convolution algorithm eliminates the need for large, duplicated patch matrices (as in im2col) by constructing a compact array of pointers to relevant input regions, allowing the kernel to operate directly on the native memory layout and significantly reducing both memory footprint and redundant bandwidth (Dukhan, 2019).
2. Asynchronous Cache Swapping and Offloading: LightCache (Xiao et al., 6 Oct 2025) implements GPU memory reduction by immediate async offload of cached intermediate feature maps to CPU-pinned memory, overlapping the transfer with useful compute. This extends the effective working set capacity using much larger, albeit slower host memory, but exploits latency-hiding and scheduled reloading.
3. In-Memory Execution/Copying (RowClone): Full-row or partial-row copies and zero-initialization are performed entirely within DRAM subarrays, enabled via back-to-back activate commands to the source and destination rows (FPM), or through a pipelined serial mechanism for inter-bank movement (PSM), limiting off-chip data movement to control commands only (Seshadri et al., 2018).
4. Near-Memory Index Decoupling and Coalescing: For indirect gather/scatter patterns (e.g., SpMV), near-memory coalescers in units such as AXI-Pack assemble many narrow, irregular accesses into handfuls of wide DRAM bursts—multiplying bandwidth efficiency and drastically reducing end-to-end latency and energy (Zhang et al., 2023).
5. Address-Range Remapping and Locality Migration: Distributed address-indirection tables (as in DL-PIM) allow dynamically redirecting requests to the “current” local data copy in processing-in-memory subsystems, supporting proactive or demand-driven data migration with adaptive, workload-aware enabling/disabling (Tian et al., 9 Oct 2025).
6. Compression for Data Path Bandwidth Virtualization: Genomics acceleration by Bancroft (Lim et al., 23 Feb 2025) achieves indirect memory acceleration by blockwise reference-based compression at the boundary between PCIe and HBM. Massive effective dataset sizes (e.g., TB–PB) can be accessed by hardware kernels at near-HBM rates, using only limited HBM-resident structures and decompressors.

3. Quantitative Impact in Application Domains

Indirect memory acceleration delivers significant measured improvements in diverse workloads. Selected empirical data:

Domain	Strategy	Throughput/Speedup	Resource Impact	Reference
Video diffusion	Cache offloading, chunking	Up to 3× speedup, 30–60% lower GPU memory	Maintains ≤0.1 LPIPS degradation	(Xiao et al., 6 Oct 2025)
Genomics PCIe-FPGA	Compression, indirection	6×–30× speedup (realistic use)	Delivers up to 30% of raw HBM BW with small HBM use	(Lim et al., 23 Feb 2025)
SpMV (vector/HPC)	Near-memory coalescing	8× indirect-access BW, 3× SpMV speedup	∼0.3 mm², 27 kB SRAM coalescer	(Zhang et al., 2023)
DRAM data movement	In-DRAM RowClone (FPM/PSM)	Up to 11.6× latency, 74× energy reduction	Area +∼0.2%, limited to intra-subarray/bank	(Seshadri et al., 2018)
PIM/3D-stacked memory	Locality indirection table	Latency ↓54% (HMC), speedup 6% (avg), 15% (reuse)	Up to +0.3% area, adaptive policy eliminates slowdowns	(Tian et al., 9 Oct 2025)
Stencil comp., RISC-V	Indirect stream registers	2.7×–3.9× speedup, 1.58× energy efficiency	FPU util↑ to 81% (8-core), 64% (256-core)	(Scheffler et al., 2024)

Context-specific results consistently demonstrate that when bottlenecks are dominated by memory bandwidth, redundant loading, or working set overflows, indirection-based approaches that manipulate access or data-movement pathways can yield multi-fold acceleration.

4. Analytical Models and Performance Trade-Offs

Indirect memory acceleration effectiveness is quantifiable through several analytical models:

• Bandwidth Utilization: Near-memory coalescers (Zhang et al., 2023) elevate effective DRAM bandwidth utilization η from η_base ≈ 1/8 (8 B/64 B) to η_coal ≈ 1, with speedup Sₑff = c̄ (average coalescing factor).
• Memory Footprint and Latency: Replacement of im2col with pointer indirection reduces temporary buffer size by a factor of O(1/C) (number of input channels) (Dukhan, 2019), shifting the bottleneck to pointer-array and accessing native layout.
• Latency and Power: In-DRAM primitives remove off-chip transfers, so copy latency can drop by up to ∼12× and energy per operation by ∼74× (Seshadri et al., 2018).
• Overlap and Scheduling Constraints: Asynchronous cache offloaded to host memory saves GPU memory at the cost of PCIe transfer, requiring compute–I/O overlap to avoid stalling (Xiao et al., 6 Oct 2025).

Trade-offs include possible increased code or hardware complexity, the need for sophisticated eviction/scheduling policies, and potential reduced benefit when data locality, reuse, or access regularity are insufficient.

5. Control Policies and Adaptive Mechanisms

Indirect memory acceleration often benefits from active or adaptive control to mitigate potential overheads in pathological cases:

• Adaptive Enabling/Disabling: DL-PIM (Tian et al., 9 Oct 2025) utilizes epoch-based feedback on actual request latency or hop distance to enable or disable indirection dynamically. Sampling “leader” sets for always/never-subscribe ensures policy robustness.
• Chunking and Slicing Hyperparameters: LightCache (Xiao et al., 6 Oct 2025) selects feature map chunk size (η, ω) and cache intervals (N) to balance memory savings, compute overhead, and I/O congestion.
• Cache and Eviction Policies: Multi-stage memory hierarchies (e.g., GPU ↔ CPU ↔ secondary storage) employ LRU-style cache eviction to prevent memory exhaustion and fragmentation.

Such policies are crucial for maintaining net acceleration across broad workload diversity, as blind indirection can cause performance regressions when data reuse is low or latency is dominated by transfer rather than computation.

6. Broader Implications and Future Directions

The indirect memory acceleration paradigm generalizes across compute domains:

• Scale-agnostic Working Set Expansion: Compression-based virtualization (Bancroft (Lim et al., 23 Feb 2025)) uncouples application scaling from memory capacity, critical for genomics, scientific simulation, and other ultra-large datasets.
• Compute–Memory Path Fusion and In-Memory Compute: Algorithms that can be mapped, even partially, onto in-memory primitives stand to benefit substantially from the minimization or complete elimination of off-chip traffic.
• Irregular and Gather-Heavy Workloads: In graph analytics, SpMV, or scatter/gather kernels, indirect access coalescing near DRAM can mitigate otherwise prohibitive overhead from fragmented access patterns (Zhang et al., 2023, Khadem et al., 29 May 2025).
• Algorithmic Memory-Aware Scheduling: Control protocols exploiting system memory effects (multi-modal relaxations, memory effects in optimization/dynamics) enable new routes for system-level acceleration rooted in modeling and exploiting the full coupled system dynamics (Ibáñez et al., 13 Mar 2026).

Limitations currently center on domains where local reuse is minimal, pointer indirection or offloading incurs overhead exceeding baseline access, or if indirection breaks programming or coherency assumptions. Potential future work includes extending compression/indirection to writes, in-memory scatter, or more general transactional semantics, and co-design of memory-acceleration hardware/software stacks for emerging memory technologies.