arXiv subjects

Animesh Basak Chowdhury

Publications and source records attributed to Animesh Basak Chowdhury.

16 recordsLinked to original sources

arXiv2025

David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?

Large Language Model(LLM) inference demands massive compute and energy, making domain-specific tasks expensive and unsustainable. As foundation models keep scaling, we ask: Is bigger always better for hardware design? Our work tests this by evaluating Small Language Models coupled with a curated agentic AI framework on NVIDIA's Comprehensive Verilog Design Problems(CVDP) benchmark. Results show that agentic workflows: through task decomposition, iterative feedback, and correction - not only unlock near-LLM performance at a fraction of the cost but also create learning opportunities for agents, paving the way for efficient, adaptive solutions in complex design tasks.

cs.LG↗

arXiv2024

ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search

Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, neglecting all the complexities and potential side-effects for security arising from the design automation process. That is, automation traditionally prioritizes power, performance, and area (PPA), sidelining security. We propose a "security-first" approach, refining the logic synthesis stage to enhance the overall resilience of PSC countermeasures. We introduce ASCENT, a learning-and-search-based framework that (i) drastically reduces the time for post-design PSC evaluation and (ii) explores the security-vs-PPA design space. Thus, ASCENT enables an efficient exploration of a large number of candidate netlists, leading to an improvement in PSC resilience compared to regular PPA-optimized netlists. ASCENT is up to 120x faster than traditional PSC analysis and yields a 3.11x improvement for PSC resilience of state-of-the-art PSC countermeasures

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arXiv2024

Scalable Test Generation to Trigger Rare Targets in High-Level Synthesizable IPs for Cloud FPGAs

High-Level Synthesis (HLS) has transformed the development of complex Hardware IPs (HWIP) by offering abstraction and configurability through languages like SystemC/C++, particularly for Field Programmable Gate Array (FPGA) accelerators in high-performance and cloud computing contexts. These IPs can be synthesized for different FPGA boards in cloud, offering compact area requirements and enhanced flexibility. HLS enables designs to execute directly on ARM processors within modern FPGAs without the need for Register Transfer Level (RTL) synthesis, thereby conserving FPGA resources. While HLS offers flexibility and efficiency, it also introduces potential vulnerabilities such as the presence of hidden circuitry, including the possibility of hosting hardware trojans within designs. In cloud environments, these vulnerabilities pose significant security concerns such as leakage of sensitive data, IP functionality disruption and hardware damage, necessitating the development of robust testing frameworks. This research presents an advanced testing approach for HLS-developed cloud IPs, specifically targeting hidden malicious functionalities that may exist in rare conditions within the design. The proposed method leverages selective instrumentation, combining greybox fuzzing and concolic execution techniques to enhance test generation capabilities. Evaluation conducted on various HLS benchmarks, possessing characteristics of FPGA-based cloud IPs with embedded cloud related threats, demonstrates the effectiveness of our framework in detecting trojans and rare scenarios, showcasing improvements in coverage, time efficiency, memory usage, and testing costs compared to existing methods.

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arXiv2024

Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS

Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) efficiency. This is due to the lack of PPA awareness in conventional transformer decoding algorithms. In response, we present an automated transformer decoding algorithm that integrates Monte Carlo tree-search for lookahead, guiding the transformer to produce compilable, functionally correct, and PPA-optimized code. Empirical evaluation with a fine-tuned language model on RTL codesets shows that our proposed technique consistently generates functionally correct code compared to prompting-only methods and effectively addresses the PPA-unawareness drawback of naive large language models. For the largest design generated by the state-of-the-art LLM (16-bit adder), our technique can achieve a 31.8% improvement in the area-delay product.

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arXiv2024

Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization

Logic synthesis, a pivotal stage in chip design, entails optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates. The process involves a sequential application of logic minimization heuristics (``synthesis recipe"), with their arrangement significantly impacting crucial metrics such as area and delay. Addressing the challenge posed by the broad spectrum of design complexities - from variations of past designs (e.g., adders and multipliers) to entirely novel configurations (e.g., innovative processor instructions) - requires a nuanced `synthesis recipe` guided by human expertise and intuition. This study conducts a thorough examination of learning and search techniques for logic synthesis, unearthing a surprising revelation: pre-trained agents, when confronted with entirely novel designs, may veer off course, detrimentally affecting the search trajectory. We present ABC-RL, a meticulously tuned $α$ parameter that adeptly adjusts recommendations from pre-trained agents during the search process. Computed based on similarity scores through nearest neighbor retrieval from the training dataset, ABC-RL yields superior synthesis recipes tailored for a wide array of hardware designs. Our findings showcase substantial enhancements in the Quality-of-result (QoR) of synthesized circuits, boasting improvements of up to 24.8% compared to state-of-the-art techniques. Furthermore, ABC-RL achieves an impressive up to 9x reduction in runtime (iso-QoR) when compared to current state-of-the-art methodologies.

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arXiv2023

Towards the Imagenets of ML4EDA

Despite the growing interest in ML-guided EDA tools from RTL to GDSII, there are no standard datasets or prototypical learning tasks defined for the EDA problem domain. Experience from the computer vision community suggests that such datasets are crucial to spur further progress in ML for EDA. Here we describe our experience curating two large-scale, high-quality datasets for Verilog code generation and logic synthesis. The first, VeriGen, is a dataset of Verilog code collected from GitHub and Verilog textbooks. The second, OpenABC-D, is a large-scale, labeled dataset designed to aid ML for logic synthesis tasks. The dataset consists of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs on a large number of open-source hardware projects. In this paper we will discuss challenges in curating, maintaining and growing the size and scale of these datasets. We will also touch upon questions of dataset quality and security, and the use of novel data augmentation tools that are tailored for the hardware domain.

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arXiv2023

INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search

Logic synthesis is the first and most vital step in chip design. This steps converts a chip specification written in a hardware description language (such as Verilog) into an optimized implementation using Boolean logic gates. State-of-the-art logic synthesis algorithms have a large number of logic minimization heuristics, typically applied sequentially based on human experience and intuition. The choice of the order greatly impacts the quality (e.g., area and delay) of the synthesized circuit. In this paper, we propose INVICTUS, a model-based offline reinforcement learning (RL) solution that automatically generates a sequence of logic minimization heuristics ("synthesis recipe") based on a training dataset of previously seen designs. A key challenge is that new designs can range from being very similar to past designs (e.g., adders and multipliers) to being completely novel (e.g., new processor instructions). %Compared to prior work, INVICTUS is the first solution that uses a mix of RL and search methods joint with an online out-of-distribution detector to generate synthesis recipes over a wide range of benchmarks. Our results demonstrate significant improvement in area-delay product (ADP) of synthesized circuits with up to 30\% improvement over state-of-the-art techniques. Moreover, INVICTUS achieves up to $6.3\times$ runtime reduction (iso-ADP) compared to the state-of-the-art.

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arXiv2023

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks' accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks' accuracies drops to around 50\% for ALMOST-synthesized circuits, all while not undermining design optimization.

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arXiv2022

GreyConE: Greybox fuzzing+Concolic execution guided test generation for high level design

Exhaustive testing of high-level designs pose an arduous challenge due to complex branching conditions, loop structures and inherent concurrency of hardware designs. Test engineers aim to generate quality test-cases satisfying various code coverage metrics to ensure minimal presence of bugs in a design. Prior works in testing SystemC designs are time inefficient which obstruct achieving the desired coverage in shorter time-span. We interleave greybox fuzzing and concolic execution in a systematic manner and generate quality test-cases accelerating test coverage metrics. Our results outperform state-of-the-art methods in terms of number of test cases and branch-coverage for some of the benchmarks, and runtime for most of them.

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arXiv2022

Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pre-trained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x run-time improvement and better quality-of-result (QoR) than state-of-the-art machine learning approaches.

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arXiv2022

Fuzzing+Hardware Performance Counters-Based Detection of Algorithm Subversion Attacks on Post-Quantum Signature Schemes

NIST is standardizing Post Quantum Cryptography (PQC) algorithms that are resilient to the computational capability of quantum computers. Past works show malicious subversion with cryptographic software (algorithm subversion attacks) that weaken the implementations. We show that PQC digital signature codes can be subverted in line with previously reported flawed implementations that generate verifiable, but less-secure signatures, demonstrating the risk of such attacks. Since, all processors have built-in Hardware Performance Counters (HPCs), there exists a body of work proposing a low-cost Machine Learning (ML)-based integrity checking of software using HPC fingerprints. However, such HPC-based approaches may not detect subversion of PQC codes. A miniscule percentage of qualitative inputs when applied to the PQC codes improve this accuracy to 98%. We propose grey-box fuzzing as a pre-processing step to obtain inputs to aid the HPC-based method.

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arXiv2021

FuCE: Fuzzing+Concolic Execution guided Trojan Detection in Synthesizable Hardware Designs

High-level synthesis (HLS) is the next emerging trend for designing complex customized architectures for applications such as Machine Learning, Video Processing. It provides a higher level of abstraction and freedom to hardware engineers to perform hardware software co-design. However, it opens up a new gateway to attackers to insert hardware trojans. Such trojans are semantically more meaningful and stealthy, compared to gate-level trojans and therefore are hard-to-detect using state-of-the-art gate-level trojan detection techniques. Although recent works have proposed detection mechanisms to uncover such stealthy trojans in high-level synthesis (HLS) designs, these techniques are either specially curated for existing trojan benchmarks or may run into scalability issues for large designs. In this work, we leverage the power of greybox fuzzing combined with concolic execution to explore deeper segments of design and uncover stealthy trojans. Experimental results show that our proposed framework is able to automatically detect trojans faster with fewer test cases, while attaining notable branch coverage, without any manual pre-processing analysis.

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arXiv2021

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

Logic synthesis is a challenging and widely-researched combinatorial optimization problem during integrated circuit (IC) design. It transforms a high-level description of hardware in a programming language like Verilog into an optimized digital circuit netlist, a network of interconnected Boolean logic gates, that implements the function. Spurred by the success of ML in solving combinatorial and graph problems in other domains, there is growing interest in the design of ML-guided logic synthesis tools. Yet, there are no standard datasets or prototypical learning tasks defined for this problem domain. Here, we describe OpenABC-D,a large-scale, labeled dataset produced by synthesizing open source designs with a leading open-source logic synthesis tool and illustrate its use in developing, evaluating and benchmarking ML-guided logic synthesis. OpenABC-D has intermediate and final outputs in the form of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs plus labels such as the optimized node counts, and de-lay. We define a generic learning problem on this dataset and benchmark existing solutions for it. The codes related to dataset creation and benchmark models are available athttps://github.com/NYU-MLDA/OpenABC.git. The dataset generated is available athttps://archive.nyu.edu/handle/2451/63311

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arXiv2021

ASSURE: RTL Locking Against an Untrusted Foundry

Semiconductor design companies are integrating proprietary intellectual property (IP) blocks to build custom integrated circuits (IC) and fabricate them in a third-party foundry. Unauthorized IC copies cost these companies billions of dollars annually. While several methods have been proposed for hardware IP obfuscation, they operate on the gate-level netlist, i.e., after the synthesis tools embed the semantic information into the netlist. We propose ASSURE to protect hardware IP modules operating on the register-transfer level (RTL) description. The RTL approach has three advantages: (i) it allows designers to obfuscate IP cores generated with many different methods (e.g., hardware generators, high-level synthesis tools, and pre-existing IPs). (ii) it obfuscates the semantics of an IC before logic synthesis; (iii) it does not require modifications to EDA flows. We perform a cost and security assessment of ASSURE.

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arXiv2021

Adversarially Robust Learning via Entropic Regularization

In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a specially designed distribution in the data space that assigns high probability to points with high loss and in the immediate neighborhood of training samples. Our proposed algorithms optimize this loss to seek adversarially robust valleys of the loss landscape. Our approach achieves competitive (or better) performance in terms of robust classification accuracy as compared to several state-of-the-art robust learning approaches on benchmark datasets such as MNIST and CIFAR-10.

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arXiv2019

RERS-Fuzz : Combining Greybox Fuzzing with Interval Analysis for error reachability in reactive softwares

Fuzz Testing is a well-studied area in the field of Software Maintenance and Evolution. In recent years, coverage-based Greybox fuzz testing has gained immense attention by discovering critical security level and show-stopper bugs in industrial grade software. Greybox fuzz-testing uses coverage maximization as objective function and achieve the same by employing feedback-driven evolutionary algorithms. In our work, we have utilized the power of Greybox fuzz testing, combined with interval analysis for solving reachability problem in sequential and industrial RERS (Rigorous Examination of Reactive Software) 2019 benchmarks.

cs.SE↗