Data Packing

Data packing directly addresses the memory wall by ensuring every bit transferred from DRAM to the compute fabric carries useful numerical information. Instead of wasting the upper bits of a 32-bit word when storing an 8-bit quantized weight, packing places four INT8 values into a single 32-bit transaction. This effectively quadruples the effective bandwidth for low-precision inference workloads, a critical factor for real-time RF modulation classification where IQ sample streams are continuous and high-throughput.

Modern FPGA DSP slices and CPU SIMD units (like AVX-512 or NEON) operate on wide vector registers. Data packing aligns the storage format with the compute width. For example, a 512-bit vector register can process 64 packed INT8 multiply-accumulate operations in a single cycle. Without packing, the same register would waste 75% of its compute capacity on zero-padded data. This alignment is essential for achieving peak Multiply-Accumulate (MAC) throughput on custom Deep Learning Processor Unit (DPU) architectures.

When a low-precision tensor is naively stored in a high-precision container, the unused most-significant bits are simply set to zero. This zero-padding represents pure overhead—it consumes memory, burns data transfer energy, and occupies valuable cache lines without contributing to model accuracy. Data packing is the systematic elimination of this redundancy. In a Mixed-Precision Quantization scheme, a dedicated hardware packer/unpacker unit reorganizes disparate bit-widths (e.g., 4-bit, 8-bit) into a dense, contiguous bitstream for efficient DMA transfer.

CPU and FPGA caches fetch data in fixed-size blocks called cache lines (typically 64 bytes). If a single FP32 weight occupies 4 bytes, a cache line holds 16 weights. If that weight is quantized to INT8 and packed, the same cache line now holds 64 weights. This drastically reduces cache miss rates during inference. For a Streaming Architecture processing IQ samples, packing ensures the prefetcher can keep the compute pipeline saturated with minimal stalling, directly reducing classification latency.

The primary trade-off in data packing is the unpacking logic required to extract individual elements before they enter the arithmetic datapath. This logic consumes a small amount of FPGA LUT resources or CPU shift/mask instructions. Efficient designs use a Ping-Pong Buffer scheme where one buffer is unpacking the next tile of data while the systolic array processes the current tile, effectively hiding the unpacking latency behind computation. The overhead is typically negligible compared to the bandwidth savings.

The packing dimension matters significantly for downstream compute patterns:

• Channel-major packing: Groups elements along the input channel dimension. Ideal for Depthwise Separable Convolution where spatial and channel filtering are decoupled.
• Batch-major packing: Groups elements across batch samples. Useful for high-throughput inference serving multiple IQ streams simultaneously.
• Spatial packing: Groups adjacent pixels or time samples. Beneficial for exploiting spatial locality in convolutional feature maps. The optimal strategy is often determined by a Roof-line Model analysis of the specific accelerator.

Memory bandwidth is often the primary bottleneck in accelerator performance, not compute. Data packing directly addresses this by increasing the effective information density of each memory transaction.

• The Problem: Reading a single 8-bit weight from a 64-bit memory bus wastes 87.5% of the available bandwidth
• The Solution: Packing eight 8-bit weights into one 64-bit word achieves 100% bandwidth utilization
• Impact: For a modulation classifier with millions of weights, packing can reduce memory traffic by up to 4x when moving from FP32 to INT8 precision

Operator fusion combines multiple consecutive neural network layers into a single computational kernel, keeping packed data in on-chip registers and eliminating intermediate memory round-trips.

• Synergy with Packing: Fused operators can maintain packed data formats throughout the entire fused pipeline without costly unpack/repack operations
• Common Fusions: Convolution + BatchNorm + ReLU, or MatMul + BiasAdd + Activation
• Benefit: Reduces DRAM accesses and kernel launch overhead, critical for real-time IQ sample processing on FPGAs

Ping-pong buffering is a double-buffering technique that decouples data loading from computation, allowing the accelerator to overlap memory transfers with packed data processing.

• Operation: While Buffer A is being filled with packed input data via DMA, the compute engine processes Buffer B
• Continuous Streaming: Essential for maintaining throughput in streaming RF inference pipelines where IQ samples arrive continuously
• Implementation: Requires careful alignment of packed data structures to match the DMA burst size for optimal transfer efficiency

Mixed-precision quantization assigns different bit-widths to different layers, creating heterogeneous packing requirements that demand sophisticated data layout strategies.

• Layer-Specific Precision: First layer may use INT8 for input fidelity, while hidden layers use INT4 for maximum compression
• Packing Challenge: A 128-bit vector might hold sixteen INT8 values or thirty-two INT4 values, requiring dynamic unpacking logic
• Hardware Support: Modern DPUs include native support for mixed-precision packed formats, automatically handling alignment and sign extension

Integer-only inference eliminates floating-point arithmetic entirely, making data packing straightforward and deterministic for deployment on fixed-point FPGA DSP slices.

• Quantization Scheme: Weights and activations are represented as pure integers with a shared scale factor applied only at the output
• Packing Advantage: Integer formats (INT8, INT4) pack cleanly into standard memory words without the complex mantissa/exponent alignment of floats
• Hardware Mapping: Directly maps to FPGA DSP48 slices configured for integer multiply-accumulate operations, maximizing fabric utilization