F-BFQ Accelerator: Flexible BFP Quantization for LLMs

Updated 16 October 2025

Flexible Block FloatingPoint Quantization (F-BFQ) Accelerator is a hardware system that uses block floating point quantization to optimize LLM and DNN inference on resource-constrained devices.
It implements dynamic switching between multiple quantization schemes (such as Q2 and Q3) via lightweight hardware multiplexing, enabling mixed-precision processing across layers.
Empirical results show up to 1.4× speedup and reduced memory and compute demands, facilitating efficient on-device inference for edge applications.

A Flexible Block FloatingPoint Quantization (F-BFQ) Accelerator is a specialized hardware system designed to efficiently execute LLMs and other deep neural networks (DNNs) on resource-constrained platforms. It achieves efficiency by exploiting block floating point (BFP) quantization, wherein blocks of numbers share exponents and use reduced-precision integer mantissas, enabling significant savings in both storage and compute complexity without heavily degrading model accuracy. F-BFQ accelerators are distinguished by their ability to dynamically support multiple BFP quantization schemes—crucial for supporting mixed precision across heterogeneous layers in modern LLMs—without costly hardware reconfiguration.

1. BFP Quantization Principles and Motivation

Block floating point quantization is based on the principle that subsets ("blocks") of tensor elements often have similar magnitudes. Instead of representing each number in standard floating point (distinct exponents per number), BFP structures assign a common exponent to all elements in a block, with each element carrying a fixed-point mantissa. This reduces per-element bitwidth and aligns well with the numerical distributions encountered in LLM and CNN weights/activations.

Given a block of $n$ numbers, $\{x_i\}$ , each is quantized as

$x_i \approx m_i \times 2^{e_{\text{block}}}$

where $e_{\text{block}}$ is chosen (often as maximum $e_i$ in the block), and $m_i$ is the (shifted/rounded) mantissa.

This format yields a per-value bitwidth that is markedly lower than full-precision FP, lowering memory and bandwidth demands (a particularly critical issue in edge LLM inference). Experimental results across a range of architectures, including ResNet, VGG-16, GoogLeNet, and LLMs, demonstrate that even with mantissa widths as low as 4 or 8 bits, BFP quantization can maintain accuracy within 0.3–1% of the original model (Song et al., 2017, Wu et al., 2020, Haris et al., 15 Oct 2025).

The flexibility of BFP is particularly well-suited to quantized model deployment: early LLM layers—being more robust to quantization noise—may use lower precision than later, more sensitive layers, necessitating a mixed BFP regime.

2. F-BFQ Accelerator Architecture

The F-BFQ accelerator provides pipeline parallelism, high on-chip memory bandwidth, and dynamic switching between different quantization variants without hardware reconfiguration (Haris et al., 15 Oct 2025). The principal architectural components include:

Instruction decoder for configuration, data movement, and scheduling
Data loader and scheduler for input partitioning, tiling, and synchronization
Dynamic Super-Block Processor (DSBP):
- Local caches for weights and activations
- Support for multiple BFP formats (e.g., Q2 with 2-bit weights; Q3 with 3-bit weights and associated block/scaling factors)
- Vector compute units with a shared dot product kernel and format-specific scaling units
- Multiplexers for selecting variant-specific accumulation paths

The accelerator uses dedicated control registers to select the quantization format on-the-fly. The vector engine executes the core integer dot products (taking advantage of shared scaling), and quantization-specific post-processing ensures correct output scaling and saturation.

Variant-specific configurations are used as follows:

Q3: Each superblock divided into blocks; each block carries a 6-bit scaling factor and a 16-bit super-scaling factor; overall bitwidth ≈ 3.5 bits/weight.
Q2: 2-bit weights, 4-bit min and scalar per block; overall rate ≈ 2.6 bits/weight.

Natively, the F-BFQ pipeline can tile MatMul operations according to layer shape, optimizing parallelism and reuse, and can process different variants for each layer or block as required.

3. Dynamic Quantization Support

Modern deep learning models, especially LLMs, deploy mixed quantization—different layers or tensor blocks use different quantization formats or bitwidths to balance accuracy and hardware cost. The F-BFQ accelerator is designed to dynamically switch between at least two BFP variants (demonstrated with Q2 and Q3) without interrupting the compute pipeline or requiring reconfiguration:

When an opcode is received (e.g. to initiate a MatMul), the driver sets a register (e.g. "weight_type") to select which quantization variant to apply.
The DSBP’s vector unit then routes computations through the corresponding format-specific scalar and accumulation logic.
By sharing most datapaths across variants, only lightweight multiplexing is needed; format-specific post-processing (e.g., bit shifting and rescaling) is isolated to small hardware modules.

This capability enables the accelerator to execute models (e.g., Llama.cpp-style LLMs) with per-layer quantization heterogeneity, directly addressing the fact that different blocks or layers have differing quantization tolerances.

4. Performance Metrics and Empirical Results

The F-BFQ accelerator was implemented on AMD KV260/Kria FPGA boards and benchmarked using three LLMs (GPT2, MobileLLaMA, TinyLlama) within the SECDA-LLM platform. Empirical performance includes:

Mean inference speedup: 1.4× over NEON-optimized Arm CPU
Token generation rate: Achieved 5.2 tokens/s (~3.9 words/s) aggregate; e.g., GPT2 model reduced per-sequence inference time from 1.85s (CPU) to 1.58s (F-BFQ), and improved tokens/s from 8.31 to 12.18.
Resource utilization: FPGA resource use remained within bounds, e.g., 81% of available BRAM and 14% of available DSPs.

By compressing weights and activations from FP16 or INT8 down to an average of 2.6–3.5 bits/weight in real-world Q2/Q3 BFP configurations, both memory and computational efficiency are substantially improved.

5. Deployment Scenarios and Real-world Applications

F-BFQ supports deployment of large models on devices with limited memory, compute, and energy budgets, making it suitable for:

On-device LLM inference (speech-to-text, NLU, translation)
IoT and edge AI (privacy-preserving, low-latency applications)
Robotics, gaming, and real-time mobile computation
Embedded systems with tight power/memory constraints

Dynamic quantization support is critical for scenarios where per-block or per-layer precision adaptation is required in response to workload heterogeneity.

6. Future Directions and Research Implications

The F-BFQ architecture sets a precedent for native support of variable BFP formats in hardware, paving the way for:

Additional variant support (e.g. Q4–Q8 or fine-grained user-defined formats) to further optimize LLM accuracy-efficiency tradeoffs
Extension towards more sophisticated dataflows and on-chip interconnects to minimize off-chip bandwidth bottlenecks in multi-layer models
Enhanced dynamic path scheduling—including possible per-token or per-batch format adaptation in LLM inference
Integration with quantization-aware training or hardware/software co-design tools to select BFP configurations most suited for specific deployment targets

Currently, the architecture is best suited to inference; extending it to support flexible training (e.g., with stochastic rounding or adaptive precision) would require more advanced format management.

Summary Table: Supported Quantization Variants in F-BFQ

Variant	Weight Bitwidth	Scaling Info	Approx. Bits/Weight
Q2	2	4-bit min, 4-bit scalar	2.6
Q3	3	6-bit block, 16-bit super	3.5

Use of per-variant scaling logic and runtime configuration enable fast switching between these regimes.

A plausible implication is that the approach demonstrated by F-BFQ—runtime-preemptive adaptation to layer-specific quantization formats while preserving pipeline throughput—could become foundational as future models and hardware pursue ultra-low latency, real-time deployment of LLMs and other highly parameterized networks on edge hardware (Haris et al., 15 Oct 2025).