---
title: 'IMAX3: General-Purpose CGLA Accelerator'
url: https://www.emergentmind.com/topics/imax3
type: topic
---

# IMAX3: General-Purpose CGLA Accelerator

Searching arXiv for papers on IMAX3 and related sources.
IMAX3 is a general-purpose coarse-grained reconfigurable accelerator described as a CGLA (Coarse-Grained Linear Array), presented as an “evolution” of a CGRA and evaluated through the implementation of the primary computational kernels from the `stable-diffusion.cpp` image generation framework [2511.02530]. The term should be distinguished from IMaX, the Imaging Magnetograph eXperiment for the Sunrise balloon-borne solar observatory, which is unrelated in function and domain and does not mention “IMAX3” explicitly [1009.1095]. In the accelerator literature represented here, IMAX3 denotes a linear, programmable spatial-computing platform intended to retain flexibility across workloads such as sparse GEMM, FFT, CNNs, LLMs, and, in the cited evaluation, Stable Diffusion; it is therefore positioned between highly programmable CPUs/GPUs and highly specialized fixed-function AI ASICs [2511.02530].

## 1. Terminology and scope

The available arXiv evidence uses “IMAX3” in a specific accelerator context rather than as a continuation of the solar-physics instrument named IMaX. The 2010 IMaX paper is explicitly about the Imaging Magnetograph eXperiment, a spectropolarimeter for Sunrise, and the paper “does **not** mention the term **‘IMAX3’** explicitly, nor does it define any third-generation instrument, ‘IMaX-3,’ or later upgrade carrying that exact name” [1009.1095]. That source is therefore relevant only for disambiguation.

Within the accelerator context, IMAX3 is described as a general-purpose computational platform based on a CGLA, with a linear arrangement of processing resources and interleaved processing and memory structures [2511.02530]. Its evaluation through Stable Diffusion is framed as a test of whether a general-purpose reconfigurable design can execute modern generative-AI kernels efficiently without being restricted to a single model family [2511.02530].

A common source of confusion is the orthographic similarity between **IMaX** and **IMAX3**. The former is a balloon-borne solar imaging spectropolarimeter using dual-LCVR modulation, dual-beam polarimetry, and a LiNbO\(_3\) etalon [1009.1095]. The latter is a CGLA accelerator for quantized AI kernels [2511.02530]. No naming lineage is established by the cited papers.

## 2. Architectural organization

IMAX3 is characterized by a **linear arrangement** of processing resources rather than a 2D mesh-style reconfigurable fabric [2511.02530]. Its defining organization is the interleaving of **Processing Elements (PEs) and cache memory** along that linear structure, which is intended to absorb irregular memory-access latency, improve use of **Local Memory Modules (LMMs)**, reduce memory-access bottlenecks, and preserve programmability across multiple kernel classes [2511.02530].

Each PE contains an **ALU**, **local memory**, and a **pipelined structure** to maintain throughput [2511.02530]. The architecture supports **logical placement of functional units** and “**logically aligned execution patterns**,” which are presented as a design philosophy for improving both compilation efficiency and execution efficiency [2511.02530]. The paper does not provide a full microarchitectural exposition, but it does identify IMAX3 as a spatial compute fabric rather than a purely scalar or vector coprocessor.

IMAX3 is also explicitly **multi-lane**. A lane appears as an independently operating linear PE array, and the system is reported in an **8-lane configuration** on **four AMD Versal VPK180** devices, while the Stable Diffusion evaluation on FPGA uses **one lane at 145 MHz** [2511.02530]. Table I notes that IMAX3 has **64 cores per lane**, where “core” means PE [2511.02530]. This lane structure is important because later results show that the architecture’s scaling behavior is limited not simply by PE count but by host-orchestration capacity.

At the SoC level, IMAX3 is implemented with a **Processing System (PS)** containing an **ARM Cortex-A72** and a **Programmable Logic (PL)** region containing the IMAX CGLA core, with PS and PL connected via **NoC** [2511.02530]. The PS runs the OS and handles system control, whereas the PL hosts the spatial compute array [2511.02530]. This host–accelerator coupling is central to the reported system behavior.

## 3. Memory system, execution model, and instruction support

The cited evaluation emphasizes that IMAX3’s performance cannot be understood from PE computation alone. The memory and control structures mentioned include **cache memory interleaved with PEs**, **Local Memory Modules (LMM)**, **main memory**, a **DMA buffer**, and host-side DDR4 [2511.02530]. For the FPGA platform, the listed capacities are **8 GB DDR4 for OS buffer** and **4 GB DDR4 for DMA buffer**; for projected ASIC power estimation, the paper mentions a **512 KB LMM configuration** [2511.02530].

The execution-time decomposition is given in terms of **LOAD**, **DRAIN**, **EXEC**, **CONF**, and **REGV** [2511.02530]. These denote, respectively, main-memory-to-cache transfer, cache-to-main-memory transfer, computation on the IMAX core, command transfer, and register initialization [2511.02530]. The implied execution sequence is therefore a configured and host-managed heterogeneous workflow rather than a fully autonomous accelerator pipeline.

The paper’s description of programmability is unusually important for defining IMAX3. It is repeatedly described as **not a model-specific accelerator**, and the Stable Diffusion work reuses GGML-oriented quantized dot-product kernels previously developed for `llama.cpp` [2511.02530]. That reuse is presented as evidence that the architecture supports kernels across disparate model families rather than only a single fixed workload.

To support GGML quantization formats efficiently, the implementation adds concrete custom instructions [2511.02530]:

| Instruction | Function |
|---|---|
| **OP\_SML8** | 2-way SIMD signed 8-bit integer multiply-add with sign-extended 24-bit output |
| **OP\_AD24** | 2-way 24-bit integer addition for accumulation |
| **OP\_CVT53** | SIMD conversion specialized for **Q3\_K** |

These instructions show that IMAX3 is described as an extensible ISA-like accelerator rather than a completely fixed datapath [2511.02530]. A plausible implication is that the architecture’s “general-purpose” character depends not only on reconfigurable PE placement but also on selectively introduced domain-specific primitives.

## 4. Stable Diffusion implementation and supported operators

The evaluated software framework is **`stable-diffusion.cpp`**, a pure C/C++ implementation of **Stable Diffusion v1.5** using **GGML** [2511.02530]. The workload focus is not the entire model graph but the **primary quantized dot-product kernels** used under quantized models. This is crucial because the paper’s most significant limitations stem from partial rather than comprehensive offload.

The workload includes four data types: **F32**, **F16**, **Q3\_K**, and **Q8\_0** [2511.02530]. Of these, only **Q3\_K** and **Q8\_0** are implemented on IMAX3; **FP16** and **FP32** remain on the host CPU [2511.02530]. The targeted primitive is the **dot product**, identified as central to convolutions, attention, and other U-Net operations [2511.02530].

For the **Q8\_0** kernel, the IMAX dataflow performs **8-bit integer multiply and add**, aggregates partial sums into a **24-bit integer** across **every 12 PEs**, and then performs a **final multiplication with a 32-bit single-precision floating-point value** [2511.02530]. The enabling custom instructions are primarily **OP\_SML8** and **OP\_AD24** [2511.02530].

For **Q3\_K**, GGML’s format contains **6-bit scale data** and **2-bit and 1-bit quantized data** [2511.02530]. To fit IMAX’s SIMD model, the implementation converts **6-bit scale data to 5-bit** and packs **2-bit and 1-bit** segments into a unified **3-bit** form [2511.02530]. The paper states that this approximation has “almost no effect on the final calculation results” [2511.02530]. This is one of the clearest instances of hardware–software co-design in the evaluation: the original quantization format is transformed to better match the accelerator’s SIMD execution structure.

Spatial mapping is reported explicitly. The **Q3\_K** kernel is mapped across **51 of 64 PEs**, while the **Q8\_0** kernel uses **46 of 64 PEs**, yielding effective spatial utilizations of **79.7%** and **71.9%**, respectively [2511.02530]. The paper treats these values as evidence that preserving generality entails some unused resources even for hand-tailored kernels.

## 5. Experimental platform and quantitative results

The benchmark consists of generating a **512 × 512** image with **SD-Turbo**, using **1 inference step** and the prompt **“a lovely cat”** [2511.02530]. The compared hardware comprises an **ARM Cortex-A72** on the Versal host, the **IMAX3 FPGA prototype**, a projected **IMAX3 ASIC (28 nm)**, an **Intel Xeon w5-2465X**, and an **NVIDIA GTX 1080 Ti** [2511.02530].

The hardware specifications given in the paper include the following [2511.02530]:

| Platform | Key parameters |
|---|---|
| **ARM Cortex-A72 on Versal** | **2** cores, **7 nm**, **1.4 GHz**, **8 GB DDR4**, **1.5 W** |
| **IMAX3 FPGA prototype** | **64 PEs per lane**, **145 MHz**, **8 + 4 GB DDR4**, **180 W** |
| **Projected IMAX3 ASIC (28 nm)** | **64 PEs per lane**, **14.6 mm\(^2\)**, **800 MHz** in table, **840 MHz** maximum from STA |
| **Intel Xeon w5-2465X** | **16** cores, **3.1 GHz**, **512 GB DDR5**, **200 W** |
| **NVIDIA GTX 1080 Ti** | **3584** CUDA cores, **471 mm\(^2\)**, **1480 MHz**, **11 GB GDDR5X**, **250 W** |

The paper notes a small inconsistency between the table entry (**800 MHz**) and the text, which reports **840 MHz maximum operating frequency** from static timing analysis and uses **840 MHz** for projection [2511.02530].

The offload ratio is limited. The time breakdown of dot-product execution, excluding memory-copy overhead, shows that for the **Q3\_K model**, **F32** accounts for **30.7%**, **F16** for **59.0%**, and **Q3\_K** for **10.3%**; for the **Q8\_0 model**, **F32** accounts for **21.8%**, **F16** for **62.0%**, and **Q8\_0** for **16.3%** [2511.02530]. The paper states that the overall offload ratio is **less than 20%** [2511.02530]. This directly explains why kernel acceleration does not translate into large end-to-end improvements.

The reported end-to-end latencies are [2511.02530]:

| Model | Platform | Latency |
|---|---|---|
| **Q3\_K** | IMAX3 FPGA | **790.3 s** |
| **Q3\_K** | ARM Cortex-A72 standalone | **809.7 s** |
| **Q3\_K** | IMAX3 ASIC projected | **754.5 s** |
| **Q3\_K** | Intel Xeon | **59.3 s** |
| **Q3\_K** | GTX 1080 Ti | **16.2 s** |
| **Q8\_0** | IMAX3 FPGA | **654.7 s** |
| **Q8\_0** | ARM Cortex-A72 standalone | **625.1 s** |
| **Q8\_0** | IMAX3 ASIC projected | **558.0 s** |

For the FPGA-to-ASIC compute projection, the paper uses the frequency ratio \(\frac{840}{145} \approx 5.79\), stated as an approximate **5.8× reduction in IMAX computation time** [2511.02530]. Nevertheless, because most work remains on the host CPU, the end-to-end latency changes are much smaller than this compute-only factor would suggest.

## 6. Power, efficiency, scaling, and bottlenecks

The energy-efficiency metric used is **Power-Delay Product (PDP)**, defined in the paper as

\[
PDP = \text{Execution Time} \times \text{Power}
\]

[2511.02530]. The power figures used in the evaluation are **1.5 W** for the ARM Cortex-A72, **180 W** for the IMAX3 FPGA, **47.7 W** for the projected IMAX3 ASIC in the **Q8\_0** configuration, **52.8 W** for the projected IMAX3 ASIC in the **Q3\_K** configuration, **200 W** for the Xeon, and **250 W** for the GTX 1080 Ti [2511.02530].

The paper’s qualitative conclusions are that the **ARM Cortex-A72 has the lowest PDP overall**, that the projected **IMAX ASIC beats the Xeon in PDP for both Q3\_K and Q8\_0**, and that for **Q3\_K**, the projected IMAX ASIC also achieves **lower PDP than the GPU** under the paper’s phase-based accounting methodology [2511.02530]. The text also notes that these PDP comparisons are based on “power consumption during each distinct execution phase,” so the exact plotted values are not reconstructible from a simple constant-power interpretation [2511.02530].

Kernel-level findings are more favorable than end-to-end results. The discussion reports that **single-lane FPGA IMAX at 145 MHz** is already faster than the host ARM CPU on kernel execution, that the projected **840 MHz ASIC** is **competitive with a high-performance Xeon CPU** at kernel level, and that a **significant gap remains to the GPU** [2511.02530]. Exact kernel-time numeric values are not provided in the text.

Scaling behavior is explicitly host-limited. Performance improves efficiently up to **2 lanes**, but saturates for **3 or more lanes** because the **dual-core host CPU** becomes the bottleneck for feeding and controlling the lanes [2511.02530]. This is one of the paper’s central systems-level results: accelerator scaling is limited by orchestration and data supply rather than only by PE-array size.

The paper’s own explanation for the small end-to-end speedups is summarized in six points: only quantized kernels are offloaded; FP16 and FP32 dot products dominate; the offloaded fraction is **< 20%**; the host ARM CPU executes the majority of work; host control and data movement overheads are substantial; and additional IMAX lanes are bottlenecked by the **dual-core host** [2511.02530]. The weaker FPGA result for **Q8\_0** is attributed specifically to **larger data transfer volume** [2511.02530].

## 7. Interpretation, limitations, and relation to the broader IMAX name

The Stable Diffusion evaluation is explicitly a **first implementation and baseline characterization**, not a complete deployment of Stable Diffusion on IMAX3 [2511.02530]. The major limitations stated in the paper are that **only quantized dot-product kernels were implemented**, **FP16 and FP32 kernels were not offloaded**, end-to-end acceleration therefore remained small, the **dual-core host CPU limited multi-lane scaling**, and **data transfer overhead** hurt **Q8\_0** especially [2511.02530]. The evaluation also used **single-step SD-Turbo** at **512×512**, not a more demanding multi-step diffusion workload or larger-image regime [2511.02530].

Within those limits, the paper argues that IMAX3 is a credible **general-purpose AI acceleration substrate** because it successfully reuses and executes **GGML quantized kernels across both LLM and image generation workloads** [2511.02530]. This suggests that the architecture’s strongest validation lies in cross-domain kernel reuse rather than in immediate superiority on whole-application image-generation latency.

The cited design lessons for future IMAX generations are concrete: increase offload ratio by implementing **FP16 and FP32 kernels**, co-design the accelerator with a **stronger multi-core host**, improve **data-movement efficiency**, continue adding **AI-oriented instructions** while pruning unnecessary general-purpose features, and use IMAX3 as a baseline from which to derive more **AI-specialized CGLA variants** [2511.02530]. A plausible implication is that IMAX3 should be understood less as a finished endpoint than as a transitional platform for studying how much specialization can be introduced into a reconfigurable linear array without sacrificing workload breadth.

The relationship to **IMaX** is only disambiguating. The solar-instrument paper provides a foundational technical reference for the original **Imaging Magnetograph eXperiment**, including dual-LCVR modulation, dual-beam spectropolarimetry, a LiNbO\(_3\) etalon in double pass, and high-resolution observations of the Fe I 5250.2 Å line [1009.1095]. It does not define or document IMAX3, and any connection beyond name similarity would be inferential rather than established by the cited literature [1009.1095].

Source: https://www.emergentmind.com/topics/imax3