VersaQ-3D: Efficient 3D Reconstruction Framework

• VersaQ-3D is an end-to-end algorithm–architecture co-design framework that enables real-time 3D reconstruction on edge devices using calibration-free, scene-agnostic quantization techniques.
• It integrates a Walsh–Hadamard transform and discrete cosine transform to effectively address issues like saturated activations and preserve fine weight structures during quantization.
• The framework achieves over 98% of FP16 accuracy at low-precision (W4A8) while delivering up to 10.8× speedup and 99× energy efficiency compared to state-of-the-art edge GPUs.

VersaQ-3D is an end-to-end algorithm–architecture co-design framework developed to enable real-time, on-device execution of the billion-parameter Visual Geometry Grounded Transformer (VGGT) for feed-forward 3D reconstruction tasks, including camera pose estimation, depth mapping, and point cloud generation. VersaQ-3D integrates a mathematically exact, calibration-free post-training quantization (PTQ) technique with a reconfigurable hardware accelerator, both specifically tailored to the unique activation statistics and hardware demands of VGGT. The framework achieves inference at low precision without per-scene optimization or runtime calibration and supports executable precision down to 4 bits while preserving state-of-the-art accuracy and delivering significant speedup and energy efficiency over contemporary edge GPUs (Zhang et al., 28 Jan 2026).

1. Motivation: Challenges in VGGT Quantization and Deployment

VGGT, as a feed-forward 3D reconstruction model, leverages transformer-based architectures at billion-parameter scale. This scale incurs substantial memory and computation requirements that challenge the feasibility of on-device deployment. Existing quantization methods developed for LLMs exhibit poor transferability to VGGT, primarily due to two factors:

• Saturated Activation Channels: VGGT activations have channels that remain nearly saturated through most of their value range (25th–75th percentiles), rendering conventional outlier-smoothing ineffective.
• Heterogeneous 3D Semantics: The model is applied to diverse 3D scenes, and quantization methods relying on calibration data are unreliable due to scene diversity and activation instability.
• Hardware Bottlenecks: VGGT’s reliance on precision-sensitive nonlinear operations (e.g., normalization, activation) and the quadratic scaling of memory needs in global attention prevent efficient execution on existing accelerators.

These challenges necessitate a unified algorithm-architecture approach capable of robust low-precision execution and efficient hardware utilization (Zhang et al., 28 Jan 2026).

2. Algorithmic Pipeline: Calibration-Free, Scene-Agnostic Quantization

VersaQ-3D introduces the first PTQ pipeline capable of supporting VGGT at 4-bit weight (W4) and 4- or 8-bit activation (A4/A8) resolution without reliance on calibration data or per-scene tuning. The quantization pipeline includes several core steps:

2.1 Orthogonal-Transform-Based Outlier Suppression

To address activation saturation, VersaQ-3D applies a fixed, integer-efficient Walsh–Hadamard transform (WHT):

• Let $x \in \mathbb{R}^C$ denote the per-token activation vector. The transformation $z = Hx$ uses an orthonormal Hadamard matrix $H$ ($H H^\top = I$), whose entries are $\pm 1/\sqrt{C}$.
• Implementation uses only sign flips, avoiding multipliers, and is thus well-suited for integer hardware.

2.2 Weight-Domain Discrete Cosine Transform (DCT)

For weights, preservation of fine structure is critical. VersaQ-3D uses an offline one-dimensional Discrete Cosine Transform (DCT):

• The final quantized weights are $W_{final} = H^\top (\gamma W_{orig}) D$, where $D$ is the DCT matrix, $\gamma$ are fused LayerNorm or LayerScale gains, and $W_{orig}$ are the full-precision weights.

2.3 Quantization Formulation

The quantization process is defined as:

Quantity	Formula	Notes
Weight, $w_q$	$$w_q[i, j] = \text{clamp}\left(\text{round}\left(\frac{W_{final}[i, j]}{s_w}\right), -2^{b_w-1}, +2^{b_w-1}-1\right)$$	$b_w=4$ (typical); $s_w$ per-matrix
Act., $a_q$	$$a_q[k] = \text{clamp}\left(\text{round}\left(\frac{a'[k]}{s_a}\right), -2^{b_a-1}, +2^{b_a-1} - 1\right)$$	$a' = Hx$, $b_a = 4$ or $8$, per-channel

• All scaling factors ($s_w$, $s_a$) and transformation matrices are selected offline, with no need for calibration data.
• After the WHT, activations propagate in the "rotated" domain, and further WHTs are unnecessary, eliminating runtime overhead.

2.4 Invariance and Fused Normalization

The functional correctness of the quantized model is proven by orthogonality: for any linear layer $X W_{orig}$, $(X H)(H^\top W_{orig}) = X W_{orig}$. Fused LayerNorm and LayerScale absorb any scaling mismatches, ensuring the quantized model replicates FP16 behavior.

3. Accelerator Architecture: Reconfigurable Multi-Precision Compute

VersaQ-3D implements a custom 3.88 mm², 1 GHz TSMC 28 nm accelerator, featuring on-chip SRAM (≈320 KB) and a 102 GB/s LPDDR5 interface:

• Hierarchical PE Array: 128 × 128 INT4 processing elements (PEs) are aggregated as a 64 × 64 INT8 array (bit-fusion of 2 × 2 INT4 units). Four INT8 PEs share exponent/mantissa logic to create a BF16 "Brain-Float Unit" (BFU).
• INT modes utilize an output-stationary systolic array for low-latency MACs in INT4/INT8; BF16 is achieved via a 4-stage SIMD pipeline without external floating point units.
• Nonlinear Operators: LayerNorm, GeLU, Softmax, and similar operations execute directly on the BFUs, while a quantization unit (QU) streams out dequantized FP16 results.
• Precision Support: Single datapath executes INT4, INT8, and BF16 instructions, selectable for different operations.

4. Memory-Optimized Attention: Two-Stage Recomputation-Based Tiling

Global attention in VGGT yields an $S P \times S P$ score matrix over $S$ frames and $P$ patches, making buffering prohibitive. VersaQ-3D introduces a memory- and energy-efficient two-stage tiling mechanism for Multi-Head Attention (MHA):

1. Stage I (Max & Sum Update):
   • For each query tile $Q_i$ ($T_Q \times d_k$) and key tile $K_j$ ($T_K \times d_k$), compute $S_{i,j} = Q_i K_j^\top / \sqrt{d_k}$ in INT.
   • Maintain running row-max $M_i'$ and sum $\Sigma_i'$ in INT16/32, discarding intermediate scores.
2. Stage II (Softmax & Value Application):
   • Recompute $S_{i,j}$ as needed; calculate normalized attention weights via $$Softmax(S_{i,j}) = \exp(S_{i,j} - M_i') / \Sigma_i'$$.
   • Quantized, normalized scores (INT8) are multiplied with $V_j$ and streamed out as output $O_i$.

By trading recomputation (cheap integer matmul) for buffer space, peak memory is halved and attention latency is reduced by 7%, enabling on-chip execution (Zhang et al., 28 Jan 2026).

5. Performance Evaluation and Comparative Analysis

VersaQ-3D was evaluated on two 3D reconstruction benchmarks: Co3Dv2 and 7-Scenes. Its key results include:

• Accuracy vs. Prior PTQ: In W4A8 mode, VersaQ-3D preserves 98–99% of FP16 accuracy (AUC@30=0.9553 vs. 0.9719 full precision). At W4A4, VersaQ-3D outperforms RTN and QuaRot by 1.61×–2.39× on camera-pose, with only a 10–15% relative drop in point-map metrics.
• Bitwidth Sensitivity: Weights fixed at 4 bits; activations remain robust down to 4 bits, while RTN significantly degrades below 5 bits. Conversely, activating at 4 bits, VersaQ-3D tolerates 3-bit weights, whereas RTN is unstable below 5 bits.
• Algorithmic Ablation: WHT alone recovers approximately 15% of AUC@30 loss at W4A4; addition of DCT for weights recovers a further ≈30%, supporting the necessity of the two-step pipeline.

Mode	Speedup (vs. Xavier/Orin)	Energy Efficiency Gain (vs. Xavier/Orin)	Relative Accuracy (AUC@30)
W4A4	8.9×–10.8× (7-Scenes)	81.9×–99.3× (7-Scenes)	0.5617 (vs. 0.2346 prior)
W4A8	3–6×	Similar	0.9553 (vs. 0.9719 FP16)

End-to-end latency is 40% of the GPU baseline; INT4 quantization reduces model-load time by 60%, while two-stage tiling lowers attention latency by an additional 7% (Zhang et al., 28 Jan 2026).

6. Significance, Applications, and Outlook

VersaQ-3D represents the first system enabling execution of a 1.2B-parameter, feed-forward 3D reconstruction transformer in real time on edge-class hardware, maintaining greater than 98% of full-precision accuracy at W4A8 and up to 10.8× speedup and 99× energy efficiency compared to state-of-the-art edge GPUs. The calibration-free, scene-agnostic PTQ pipeline eliminates the necessity for calibration data or per-scene tuning, generalizing across arbitrarily diverse 3D scenes. The reconfigurable accelerator architecture fuses integer and floating-point operations, consolidating linear/nonlinear computation and addressing the memory bottleneck in transformer attention via recomputation-based tiling.

A plausible implication is that the principles underlying VersaQ-3D—orthogonal transform-based quantization and unified accelerator design—may inform future deployment pipelines for large-scale vision transformers and high-capacity 3D models on resource-constrained devices, beyond the specific VGGT use case (Zhang et al., 28 Jan 2026).