FedChip: Federated LLM for Chip Design

• FedChip is a federated fine-tuning approach for LLMs that generates synthesizable chip designs while preserving proprietary data and balancing power, performance, and area (PPA) constraints.
• The framework integrates cross-device federated learning, LoRA-based parameter-efficient fine-tuning, and a domain-specific Chip@k metric to validate design feasibility.
• Experimental results show FedChip nearly matches centralized training performance and boosts design quality by about 77% compared to general-purpose LLM baselines.

FedChip is a federated fine-tuning approach for LLMs in automated artificial intelligence accelerator chip design. Introduced in “FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design” (Nazzal et al., 23 Jul 2025), it addresses two constraints that limit the use of general-purpose LLMs in hardware generation: proprietary design data that cannot be pooled across organizations, and the need to satisfy conflicting power, performance, and area (PPA) objectives for synthesizable designs. The framework combines cross-device federated learning, parameter-efficient fine-tuning with LoRA, and a domain-specific evaluation metric, Chip@k, to let multiple chip design parties collaboratively improve a shared LLM while keeping raw HDL and PPA data on-premise (Nazzal et al., 23 Jul 2025).

1. Problem setting and design target

FedChip is motivated by the observation that hardware design is naturally expressed in text, including RTL/Verilog and HLS, which makes LLM-based automation plausible. The paper states, however, that state-of-the-art general-purpose LLMs such as GPT-4o, Gemini Advanced, and Claude 3.5 Sonnet often hallucinate or fail to meet domain constraints for synthesizable designs, especially for complex AI accelerators (Nazzal et al., 23 Jul 2025). Two causes are emphasized: domain data scarcity due to proprietary IP and privacy concerns, and the difficulty of balancing workload- and architecture-specific PPA objectives with precise control.

The hardware generation task in FedChip is the synthesis-oriented generation of Verilog HDL for an Approximate Tensor Processing Unit (APTPU). The APTPU is described as a systolic-array accelerator with Output-Stationary dataflow, weight and IFMap memories, FIFOs, a controller, Pre-Approximate Units (PAUs), and Approximate Processing Elements (APEs) (Nazzal et al., 23 Jul 2025). Prompts encode structural and micro-architectural constraints, including array dimensions, data widths, approximation modes, and memory tiling, and may implicitly or explicitly target PPA ranges.

This formulation makes FedChip simultaneously a code-generation system and a constrained design-space exploration mechanism. A plausible implication is that the method is less concerned with unconstrained HDL synthesis than with producing candidate designs that survive downstream physical evaluation under multi-objective acceptance tests.

2. Federated architecture and optimization procedure

FedChip adopts a standard cross-device federated learning topology with $K$ clients and a central server or aggregator. In the paper’s experiments, three representative parties, denoted A, B, and C, hold proprietary data locally and fine-tune a shared LLM on-premise; only model updates are transmitted to the server, which aggregates them with Federated Averaging (FedAvg) and redistributes the updated global model across communication rounds until convergence (Nazzal et al., 23 Jul 2025). No raw HDL or PPA data leaves client premises.

Local fine-tuning is parameter-efficient. The base model is Mistral 7B, trained with quantization-aware fine-tuning in which the model is quantized to int8 during training while critical operations remain in floating point. The LoRA configuration uses rank $r = 8$ and scaling $\alpha = 16$. Instruction formatting follows the CodeAlpaca template. The hyperparameters reported are sequence length 1024, batch size 16, learning rate $2 \times 10^{-5}$, and the AdamW optimizer; the FL framework is OpenFedLLM, with aggregation via FedAvg (Nazzal et al., 23 Jul 2025).

For token-level Verilog generation, the local loss is cross-entropy:

$$L = -\frac{1}{N} \sum_{i=1}^N \sum_{j=1}^C y_{ij} \log p_{ij},$$

where $N$ is batch size, $C$ is vocabulary size, $y_{ij}$ is one-hot ground truth, and $p_{ij}$ is the predicted token probability (Nazzal et al., 23 Jul 2025).

The federated objective is given as

$$F_i(w) = \mathbb{E}_{(x,y)\sim D_i}\left[\ell(w; x,y)\right],$$

for client $r = 8$0, and

$r = 8$1

After $r = 8$2 local epochs, FedAvg computes

$r = 8$3

The paper explicitly notes that FedChip introduces no additional optimization variants such as FedProx and no cryptographic or privacy-noise mechanisms; privacy derives from keeping raw data local and sharing only model updates (Nazzal et al., 23 Jul 2025).

The end-to-end workflow couples generation with validation. A user provides a textual design specification, the LLM generates design artifacts, primarily Verilog RTL modules, and the resulting candidates are evaluated against PPA targets using Chip@k. In parallel, federated fine-tuning proceeds under non-IID client distributions (Nazzal et al., 23 Jul 2025).

3. APTPU-Gen dataset and non-IID partitioning

To instantiate the framework, the paper introduces APTPU-Gen, a 30k-sample dataset for LLM-driven hardware design generation and federated learning. The dataset contains textual design descriptions or instructions paired with synthesizable Verilog HDL, with a top-level module present, as well as measured PPA values for area, power, and timing slack (Nazzal et al., 23 Jul 2025).

The design space consists of 29,952 variations across eight fixed systolic array sizes, including examples such as $r = 8$4, $r = 8$5, through $r = 8$6. These variants are derived from a parameterized SystemVerilog APTPU RTL, with sweeps over data width, approximation mode, and memory tiling. The paper states that multiple configurations are produced from the parameterized RTL, then synthesized and functionally verified; PPA is evaluated via the OpenROAD flow, and the resulting metrics are parsed into a structured dataset. This loop is repeated to populate the space (Nazzal et al., 23 Jul 2025).

The federated setting is deliberately non-IID. The 30k samples are clustered using K-means on PPA, followed by 20% stochastic reassignments via Dirichlet sampling, producing three sub-datasets that emulate three manufacturers with different emphases. Cluster 1 is energy-efficient, with lowest power and tighter timing; Cluster 2 tends toward larger area and higher complexity; Cluster 3 shows more negative slack or timing pressure with intermediate area. The non-IID character is quantified by KL and Jensen–Shannon divergences across cluster distributions, with the highest divergence in total power, moderate divergence in slack, and the least divergence in area (Nazzal et al., 23 Jul 2025).

This dataset construction is central to FedChip’s empirical claims because it turns federated chip-design generation into a setting where each client owns a distinct portion of the design distribution. This suggests that the reported near-centralized performance is not merely a consequence of homogeneous data partitioning.

4. Chip@k and multi-objective PPA acceptance

FedChip introduces Chip@k as a statistical acceptance metric for multi-PPA objectives. It quantifies the probability that at least one of the top-$r = 8$7 generated candidates for a design description simultaneously satisfies acceptance criteria for area, power, and slack (Nazzal et al., 23 Jul 2025). The paper emphasizes that balance across PPA is enforced at evaluation, not by a weighted scalarized training loss.

For a design $r = 8$8, if the target metrics are $r = 8$9, $\alpha = 16$0, and $\alpha = 16$1, and a generated candidate has metrics $\alpha = 16$2, the deviation for area is defined as

$\alpha = 16$3

The dataset-level standard deviation $\alpha = 16$4 is computed from the empirical distribution of ground-truth areas, and area is accepted if $\alpha = 16$5. The same construction is used analogously for power and slack, and a candidate is accepted only if it passes all three tests simultaneously (Nazzal et al., 23 Jul 2025).

For a given description $\alpha = 16$6, if $\alpha = 16$7 candidates are generated and $\alpha = 16$8 of them pass all three acceptance tests, then the probability that at least one of the top-$\alpha = 16$9 candidates is acceptable is

$2 \times 10^{-5}$0

Averaging across the distribution of descriptions $2 \times 10^{-5}$1, the metric is

$2 \times 10^{-5}$2

The paper gives an illustrative example: if $2 \times 10^{-5}$3 and $2 \times 10^{-5}$4, then for $2 \times 10^{-5}$5,

$2 \times 10^{-5}$6

There is no weighting across PPA; acceptance is conjunctive across metrics (Nazzal et al., 23 Jul 2025).

An important interpretive point is that the paper describes Chip@k as using a “Three Sigma Rule” relative to the empirical distribution, but the operational criterion stated for each metric is $2 \times 10^{-5}$7, and the appendix summarizes this as a one-sigma acceptance rule (Nazzal et al., 23 Jul 2025). The reported implementation therefore hinges on simultaneous thresholding by dataset-level standard deviations rather than on an explicitly weighted or Pareto-style objective.

5. Experimental configuration and reported results

The reported experiments use three clients, each holding one non-IID clustered sub-dataset, together with a central server. For each client, 500 samples are reserved for testing and the remainder for training. Three scenarios are evaluated: centralized training on the union of all data, FedChip federated fine-tuning, and independent training in which each party trains only on its own data. For ablation against general-purpose models, the paper also evaluates one-shot prompting with OpenAI GPT-4o, OpenAI GPT-o1, Google Gemini Advanced, and Anthropic Claude 3.5 Sonnet, all without domain-specific fine-tuning (Nazzal et al., 23 Jul 2025).

Under Chip@1, centralized training achieves 99.65%, FedChip achieves 99.30%, and the reported client-only independent models achieve 96.86% for Client 1, 95.12% for Client 2, and 93.03% for Client 3 (Nazzal et al., 23 Jul 2025). The gap between centralized and federated training is therefore 0.35%, which the paper characterizes as marginal.

Against one-shot high-end LLM baselines, the paper states that all four baselines can produce synthesizable code but largely fail to satisfy PPA constraints. FedChip is reported to improve “design quality,” defined as constraint adherence, by around 77% over the strongest vanilla baseline, GPT-o1; the paper reports this relative improvement qualitatively and does not provide detailed per-metric values or statistical tests (Nazzal et al., 23 Jul 2025).

The paper also describes distributional behavior of generated PPA. Relative to ground truth, outputs from the federated fine-tuned model cluster toward lower area and power with reduced variance, while slack is consistently positive and tightly grouped, which the paper interprets as indicating reliable timing closure in generated designs (Nazzal et al., 23 Jul 2025). No prompt–output transcripts or code snippets are included; the evidence is presented through distributional plots.

These results support two narrower conclusions. First, federated fine-tuning can approach union-of-data performance even under deliberately non-IID partitions. Second, domain adaptation appears to matter more than raw model scale for meeting joint PPA constraints in this setting. A plausible implication is that, for chip-design generation, post-training specialization dominates generic coding competence when acceptance requires physical-design viability rather than mere syntactic correctness.

6. Privacy model, EDA integration, limitations, and nomenclature

FedChip’s privacy model is data locality through federated learning. Proprietary datasets remain on-premise, and only model updates are shared for aggregation. The paper explicitly states that it does not implement or claim secure aggregation, encryption, differential privacy, or formal privacy guarantees, and that privacy–utility trade-off studies are left for future work (Nazzal et al., 23 Jul 2025). A common misconception is therefore to treat FedChip as a formally private or cryptographically protected FL system; the paper does not make that claim.

The framework is designed to sit inside an EDA-oriented pipeline. The sequence is specification to Verilog RTL generation, followed by functional and synthesis validation, then physical evaluation with the OpenROAD flow to obtain area, power, and timing slack, and finally acceptance via Chip@k over multiple generated candidates (Nazzal et al., 23 Jul 2025). This positions the LLM as a front-end design generator whose outputs are filtered by conventional downstream validation and physical evaluation rather than accepted directly.

The limitations are explicit. FedChip focuses on a single accelerator template, the APTPU, although with broad parameter sweeps. While LoRA reduces client-side cost, the work does not explore communication-efficient aggregation, personalized federated learning for non-IID data, or adaptive LoRA ranks. It also does not introduce cryptographic privacy mechanisms. The paper identifies several future directions: extending to other accelerator families, crafting advanced loss functions for variable design generations, formalizing privacy guarantees, and optimizing update and communication efficiency through methods such as rank adaptation and sparsification (Nazzal et al., 23 Jul 2025). Code and the APTPU-Gen dataset are available at the ACADLab/FedChip repository, while license terms are not specified in the paper (Nazzal et al., 23 Jul 2025).

The name “FedChip” is not unique in the arXiv record. It is also used for an unrelated monolithically integrated microwave–photonics frequency identification system on silicon-on-insulator (Yao et al., 2022), and it is orthographically close to “FedCIP,” a federated-learning intellectual-property protection framework with traitor tracking (Liang et al., 2023). In current chip-design literature, however, “FedChip” denotes the federated LLM fine-tuning framework for privacy-preserving, PPA-constrained AI accelerator generation introduced in 2025 (Nazzal et al., 23 Jul 2025).