Memory-Efficient FPGA Implementations

• Memory-efficient FPGA implementations are advanced designs that optimize on-chip storage using techniques like compression, quantization, and pruning to support high-performance computing.
• They employ sophisticated memory packing, bin-packing, and programmable controllers to reduce off-chip latency while maximizing utilization and throughput in various applications.
• On-chip-only policies and compute-in-memory strategies overcome traditional memory barriers, enabling efficient deep learning, signal processing, and combinatorial optimization on resource-constrained devices.

Memory-efficient FPGA implementations refer to the systematic design, optimization, and realization of FPGA accelerators and systems that maximize computational throughput or functionality under stringent on-chip and off-chip memory constraints. These strategies are central to a broad spectrum of applications: deep learning inference and training, compressed numerical encoders, graph analytics, scientific computing, and combinatorial optimization. Core architectural advances—including aggressive compression, sophisticated packing methodologies, on-chip-only datapaths, programmable memory controller architectures, and fused compute-in-memory—enable modern FPGAs to surpass the classical "memory wall" and deliver superior energy/performance scaling.

1. Compression, Quantization, and Pruning for On-Chip Model and Data Reduction

A foundational class of memory-efficient techniques focuses on reducing the raw storage required for model parameters, activations, and operands. Deep compression pipelines combine structured pruning (selectively removing redundant parameters), data quantization (reducing bit-widths), and compact encoding.

In RNN/LSTM inference, load-balance-aware block-wise pruning ensures that each PE's memory footprint remains equal, enabling 10× reduction in parameter count with an additional 2–3× from quantization (e.g., 12 bits/weight), achieving total ratios up to 27× without statistically significant accuracy loss. Deployment of the compressed models in on-chip column-major sparse formats such as compressed-sparse-column with relative-indexing enables highly regular block-level dataflow and matches PE array geometry (Han et al., 2016). Large DNNs with up to tens of millions of parameters can thus be supported entirely in FPGA BRAM.

For fixed-point feed-forward networks, quantize-then-retrain approaches allow mapping full networks to on-chip BRAM with as little as 3-bit precision per weight (with negligible error penalties), obviating all external DRAM weight reads. The overall on-chip storage cost for a network layer is given by

$\mathrm{bits}_k = B_k N_{k-1} N_{k}$

where $B_k$ is the bit-width, and $N_{k-1}$, $N_k$ are layer sizes (Park et al., 2016).

In sparse CNN and transformer accelerators, pruning, weight sharing, structured quantization, and factorization (e.g., tensor-train, low-rank decompositions) similarly reduce model storage by 10–50×, making full on-chip execution feasible for topologies previously considered impractical for embedded FPGAs (Gao et al., 2018, Tian et al., 11 Jan 2025).

2. Memory Packing, Bin Packing, and Buffer Utilization in CNN and Custom Designs

Efficient mapping of logical buffers to BRAM/URAM primitives is a principal source of design inefficiency in multi-buffer FPGA designs, especially for dataflow or layer-wise-pipelined CNNs. Sophisticated packing schemes such as frequency-compensated memory packing (FCMP) and evolutionary bin-packing dramatically improve effective utilization:

• FCMP exploits the frequency overprovisioning of physical RAM blocks (e.g., F_mem > F_comp), clock-multiplexing multiple logical buffers on a single BRAM and synchronizing domains via GALS islands and FIFOs. This approach enables 20–30% BRAM savings without throughput loss for small/medium nets (CIFAR-10) and with only moderate penalties (~12–30%) for large topologies such as ResNet-50, allowing porting to smaller device families (Petrica et al., 2020).
• Evolutionary bin-packing (hybrid next-fit heuristics + GA/SA) is used to group layer buffers in dataflow CNNs into physical BRAM “bins” to minimize fragmentation. With per-bin cardinality constraints (e.g., α_max=4 buffers/bin), mapping efficiency is improved up to 65%, and up to 1.65× more layers are packed into the same BRAM envelope (Kroes et al., 2020).
• In GEMM engines, weight tile scheduling is orchestrated with adaptive, heuristic load balancing: pertinent weight tiles are prefetched into UltraRAM to coincide with compute, exploiting two-phase baseline/adaptive schemes to minimize pipeline stall, while maintaining >90% URAM utilization and >80% HBM bandwidth usage even at massive parallel scales (Petropoulos et al., 9 Oct 2025).

These strategies directly convert physical mapping inefficiencies into functional capacity, unlocking the deployment of larger, deeper networks at the same cost or enabling smaller, lower-cost FPGAs to host equivalent workloads.

3. On-Chip-Only and Single-Load Policies

A systematic trend across memory-efficient FPGA accelerators is the design for on-chip-only execution: all active parameters and activations remain in BRAM or URAM during compute phases, and model parameters are loaded at most once (“single-load policy”) (Marino et al., 2024, Park et al., 2016). Key implementation tenets include:

• Vision Transformer accelerators (ME-ViT) load all weights/biases into on-chip buffers at start, retaining every intermediate feature map and bypassing external memory for residuals, normalized outputs, and skip connections. By leveraging multi-purpose buffer banks and integrating LayerNorm and Softmax pipelines inflexibly into the main compute engine, ME-ViT achieves DRAM traffic reductions up to 18× and power/throughput gains of up to 5× over standard FPGA baselines, sustaining MAC array utilization above 95% (Marino et al., 2024).
• Dataflow accelerators for CNNs maintain all weights local to each layer’s pipeline stage, streaming activations directly through FIFO chains. On-chip-only architectures, especially when combined with model quantization (e.g., binary/ternary weights) or memory packing, impose no off-chip bandwidth penalty during steady-state inference, thereby enabling dense deployment in resource-constrained edge FPGAs (Petrica et al., 2020).

This architectural policy is essential for applications featuring high compute-to-communication ratios or with stringent off-chip bandwidth caps (e.g., edge devices, embedded deployments).

4. Programmable Memory Controller Architectures

When off-chip memory accesses are unavoidable (e.g., for feature maps or large batch inferences), architectural coherence between PEs and memory controllers becomes critical (Wijeratne et al., 2021, Li et al., 2020):

• Modular memory controller designs interpose a FLIT-based interface between PEs and DRAM, supporting both cache-line and DMA transfers. Internal request schedulers batch and reorder requests to maximize DRAM row-buffer hits, while programmable internal caches and DMA engines partition traffic based on access patterns. Aggregation of these features yields up to 58% reduction in access latency and 2× sustained bandwidth improvement over fixed-function DDR IPs at the cost of moderate LUT/BRAM increases (Wijeratne et al., 2021).
• Vitis-based flows advocate careful matching of application memory access patterns to device topology: wide AXI vector widths, high occupancy for outstanding transactions, data tiling and prefetching, and array partitioning for banking. Pipelined loops are synchronized with memory-side burst transfers, systematically saturating available bandwidth for AI, graph, or streaming kernels (Li et al., 2020).

Programmable, domain-specialized memory subsystems thus translate generalized device resources into quasi-optimal memory footprints and effective throughput for heterogeneous workloads.

5. Compute-In-Memory and Bit-Serial Processing in Memory Blocks

Further memory efficiency arises from compute-in-memory (CiM) paradigms, in which computation is performed directly within or at the boundaries of memory arrays, minimizing data movement (Arora et al., 2022):

• CoMeFa (Compute-In-Memory for FPGAs) augments true dual-port BRAMs with embedded single-bit, bit-serial PEs. Each column executes programmable Boolean or arithmetic operations via per-column truth tables, with block-level carry and accumulation support, enabling, for example, matrix-vector multiplications entirely inside BRAM with only bit-serial input streaming (Arora et al., 2022).
• CoMeFa-D (delay-optimized) trades an area increase of ~3.8% die area for maximum parallelism, while CoMeFa-A (area-optimized) achieves 1.2% overhead with cycle time relaxation. Across representative deep learning and signal processing workloads, CiM yields up to 2.5× speedups and 56% energy reductions, bottlenecked only by DRAM bandwidth for memory-bound kernels.

Incorporating in-memory compute reduces on-chip routing, energy, and buffer spillage, significant for multi-tenant or array-based workloads.

6. Advanced Algorithmic/HW Codesign: Chunking, Staging, and Memory-Aware Scheduling

Applications requiring footprint beyond available BRAM/URAM, such as long-context FFT convolution or sparse graph diffusion, benefit from intelligent, memory-guided codesign (Wang et al., 28 Dec 2025, Li et al., 2021):

• Chunked FFT approaches partition the operand and filter into chunks that fit within on-chip memory, repeatedly compute partial FFT convolutions, and stitch the results together via overlap-add. This enables, for example, 450K × 450K convolutions on a 2.8 MB BRAM device with only 7% performance loss (versus a full-buffered baseline), pushing the limits of long-sequence context mixing at the edge (Wang et al., 28 Dec 2025).
• Multi-stage PPR (Personalized PageRank) breaks large-diameter, sparse graph traversals into staged diffusions on sets of small, high-priority subgraphs based on score sparsity and empirical trade-offs between memory, latency, and precision. FPGA accelerators instantiate only as many BRAM tables as required per sub-diffusion, achieving 73–8700× on-chip memory reductions and up to 700× runtime gains at typical precision levels (Li et al., 2021).
• For iterative combinatorial solvers (e.g., stochastic simulated annealing), memory use is further shrunk by integer-only temperature control, checkpointed state storage, and logic for on-the-fly result selection, yielding up to 6× memory savings with equal solution quality (Shin et al., 25 Jan 2026).

The principle is to algorithmically re-factor the problem—in the temporal or data decomposition sense—until each compute phase fits and streams efficiently within a limited on-chip memory budget.

7. Design Principles, Trade-Offs, and Impact

Memory-efficient FPGA implementation unifies compression, packing, and memory-controller co-design under a central technical principle: maximize on-chip data reuse and minimize off-chip bandwidth demands with minimal architectural or algorithmic penalty. Key trade-offs include:

• Compression/packing enable cost/density improvements but may incur LUT/FF or marginal throughput losses in large designs.
• On-chip-only policies remove I/O bottlenecks, but necessitate careful quantization, pruning, and parallelization choices to maintain model fidelity and throughput.
• Programmability and modularity increase resource usage but support flexible retargeting across workload classes and applications.
• Compute-in-memory approaches deliver high internal bandwidth but impose explicit area/frequency trade-offs.
• Algorithm-hardware co-design, including chunking and staged computation, enables the practical realization of workloads that classically exceed device memory, at controllable latency and efficiency costs.

Together, these methodologies have enabled complex inference, training, combinatorial optimization, and signal processing workloads to execute in resource- and power-constrained embedded and edge FPGAs—expanding their reach while maximizing memory utilization efficiency. Representative research achieves up to 93% capacity utilization with embedded LLMs (Li et al., 15 Feb 2025), 30–65% OCM savings in CNN accelerators (Petrica et al., 2020, Kroes et al., 2020), and order-of-magnitude improvements in throughput and energy over CPU/GPU baselines for deep models (Han et al., 2016, Gao et al., 2018, Marino et al., 2024).