OCP MX Standard for Low-Precision Compute

• Open Compute Project MX Standard is a modular framework defining blockwise data formats and arithmetic operations for efficient, low-precision computation.
• It employs hardware-friendly conversion, block scaling, and dot product accumulation to optimize neural network performance and composable memory expansion.
• The standard integrates with FPGA designs and software quantization libraries, enabling scalable inference with reduced cost and improved system interoperability.

The Open Compute Project MX Standard defines modular, open hardware and arithmetic formats for efficient low-precision computation, targeting neural network acceleration and composable memory expansion in hyperscale and enterprise environments. The specification details both data representation and operational semantics, independent of conventional per-element floating-point or fixed-point approaches. At its architectural core, MX enables block-wise scaling, hardware-friendly conversion paths, and fine control over hardware datapath area utilization—thus aligning with OCP goals of reducing system cost and promoting interoperability in composable infrastructures.

1. Standard Definition and Data Formats

The MX Standard encompasses both data formatting and required arithmetic operations for low-precision accelerators (Samson et al., 1 Jul 2024). It formalizes blockwise representations where $n$ data elements share a scale factor, either for floating-point (MXFP) or integer (MXINT) types. Concretely, supported MXFP formats include E5M2, E4M3, E3M2, E2M3, E2M1, and corresponding integer forms (e.g., INT8, INT5, INT4).

Each block consists of:

• A shared scale: typically encoded in E8M0 (8-bit exponent, 0-bit mantissa) format for MXFP, or as an integer scaling factor for MXINT.
• A sequence of private elements, each with %%%%1%%%% for floating-point, or custom integer bitwidths.

Block sizes ($k$) are implementation-defined but the reference specification assumes $k = 32$ as the default. This permits hardware to exploit block SIMD operations, minimize normalization steps, and enable error-free blockwise accumulation.

2. Arithmetic Operations and Hardware Implementation

The MX Standard mandates precise behavior for conversion, dot product, and normalization. Conversion between IEEE754 formats and MX formats requires identification of the maximum absolute value in a block, computation of a shared scale, and quantization of each private element with nearest-even rounding (Gorodecky et al., 5 Nov 2024).

Arithmetic operations, specifically dot products, adhere to the following form:

$$\operatorname{DotGeneral}(X, Y, S, T) = \sum_{c=1}^C \operatorname{Dot}(X_c, Y_c, S_c, T_c)$$

$$\operatorname{Dot}(A, B, s, t) = (s \cdot t) \sum_{p=1}^k A_p B_p$$

where $A$, $B$ are block vectors, $s$, $t$ are shared scales.

FPGA implementations utilize binary tree adder architectures for pairwise accumulation, with internal precision calculated as:

$b_{int} = 2(1 + 2^e + (M - 1))$

and the final output width:

$b_o = b_{int} + \log_2(k)$

These features ensure error-free accumulation and support block normalization, with dynamic adjustment of output widths aligned with block size $k$ and element bitwidths (Samson et al., 1 Jul 2024).

3. Conversion Algorithms and Hardware Models

MX hardware conversion is a combinational, three-step process, implemented memory-free for efficiency:

1. Maximum Extraction: Determine the highest FP32 exponent among 32 inputs via a comparator tree network (five levels for 32 elements).
2. Scale Computation: Convert the maximum exponent into a shared scale using an offset subtraction per target MX format. For $K$ exponent bits,

$$X_{\text{temp}} = \begin{cases} E_{V_i} - 2^{K-1}, & E_{V_i} > 2^{K-1} - 1 \ 0, & \text{otherwise} \end{cases}$$

With additional logic for NaN/Infinity detection.

1. Private Element Generation: For each input, compute exponent EK and quantize mantissa bits as per MX format tables, following IEEE rounding (Gorodecky et al., 5 Nov 2024).

This pipeline supports E5M2, E4M3, E3M2, E2M3, E2M1, INT8, each with specified combinations of exponent and mantissa bitwidths. Typical hardware resource utilization for the converter is reported as:

• Largest value block: ~55% LUT usage
• Scale computation: ~1% LUT usage
• Private quantization: ~44% LUT usage Critical path delays are format-dependent (e.g., E5M2: 51.4 ns, E4M3: similar).

4. Implementation-Defined Semantics and FPGA-Adaptive Choices

While the MX Standard prescribes core arithmetic and formatting requirements, it leaves several dimensions implementation-defined, such as:

• Internal precision for accumulation (Kulisch or adder-tree, integer/floating)
• Block size and element type, parameterized to trade area for inference accuracy
• Special value propagation: Handling of NaN/Inf in block formats, including blockwise or per-element flagging

These aspects are concretely demonstrated in FPGA releases, which provide IP cores supporting custom block sizes and flexible element widths, allowing rapid design-space exploration. Handling of special values in FP8/FP6/MXINT formats follows either block-flag or per-element enablement consistent with the OCP FP8 specification (Samson et al., 1 Jul 2024).

5. Software Integration and Quantization Libraries

MX standard IP cores are released open source alongside a PyTorch quantization library extension for Brevitas (Samson et al., 1 Jul 2024). Key features include:

• MX quantizers supporting minifloat and block floating-point quantization
• QAT and PTQ support for MX, including per-tensor, per-channel, and blockwise scale-sharing
• Dynamic integration of scale computation during inference, reflecting practical hardware deployment—scales are reshaped and compressed for efficient hardware representation

This integration supports end-to-end design, training, and deployment of neural networks such as ResNet-18 on ImageNet, quantized in MX formats.

6. Practical Performance and Area/Error Trade-offs

Experimental results demonstrate:

• INT5/FP6 formats (not natively supported on GPU) maintain near-baseline accuracy for networks such as ResNet-18 when QAT is applied.
• MX formats achieve fine area/error trade-offs: error vs. area plots illustrate MXINT5/6 and MXFP6/7 are highly favorable for hardware efficiency.
• FPGAs exhibit significant advantages in supporting custom datapaths (dot product with error-free accumulation and normalization) and nonstandard formats, due to reconfigurability and parameterized IP cores.

The MX standard enables scaling of effective memory and computation resource utilization—especially in hyperscale and enterprise dataplanes—with direct implications for AI model deployment.

7. Alignment with Hyperscale Memory Expansion and Economic Models

The MX Standard complements the OCP Hyperscale CXL Tiered Memory Expander specification, which details hardware-accelerated, lossless compressed memory tiers for CXL Type 3 devices (Arelakis et al., 4 Apr 2024). By leveraging blockwise compression and decompression (e.g., 2–3× memory compression, 46 GB/s throughput per 4 channels, cache line access <250 ns), MX-driven designs reduce both capital and operational expenditure:

$$\text{Effective Capacity} = \text{Physical Capacity} \times \text{Compression Ratio}$$

In practice, hyperscale deployments report TCO reductions of 20–25% while doubling or tripling usable memory without extra slots. The dynamic NUMA-exposed compressed memory tier is energy- and area-efficient ($\approx$0.9 mm$^2$ @ 4nm).

Areas for further collaborative advancement include upstream Linux driver APIs and composable pools, harmonizing the MX Standard’s modular quantization and arithmetic features with memory-rich hyperscale systems.

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The Open Compute Project MX Standard codifies a blockwise, flexible framework for low-precision computation and efficient conversion/hardware acceleration. Its adoption in FPGA and composable memory systems advances hyperscale efficiency, error-area trade-off optimization, and open engineering interoperability across diverse compute infrastructure.