Siddharth Garg
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View article: A Spatial Array for Spectrally Agile Wireless Processing
A Spatial Array for Spectrally Agile Wireless Processing Open
Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging demands such as high-frequency operation, wide bandwidths, co-ex…
View article: TrojanLoC: LLM-based Framework for RTL Trojan Localization
TrojanLoC: LLM-based Framework for RTL Trojan Localization Open
Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the design into a graph, such as a gate-level netlist or an RTL…
View article: MTU: The Multifunction Tree Unit for Accelerating Zero-Knowledge Proofs
MTU: The Multifunction Tree Unit for Accelerating Zero-Knowledge Proofs Open
Zero-Knowledge Proofs (ZKPs) are critical for privacy preservation and verifiable computation. Many ZKPs rely on kernels such as the SumCheck protocol and Merkle Tree commitments, which enable their security properties. These kernels exhib…
View article: EFFECT OF TRUNK PNF EXERCISE v/s BALANCE TRAINING IN OLD AGE INDIVIDUAL
EFFECT OF TRUNK PNF EXERCISE v/s BALANCE TRAINING IN OLD AGE INDIVIDUAL Open
Risk of fall in elderly patient
View article: Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs
Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs Open
Zero-Knowledge Proofs (ZKPs) are rapidly gaining importance in privacy-preserving and verifiable computing. ZKPs enable a proving party to prove the truth of a statement to a verifying party without revealing anything else. ZKPs have appli…
View article: Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning
Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning Open
Recent work has shown that eliciting Large Language Models (LLMs) to generate reasoning traces in natural language before answering the user's request can significantly improve their performance across tasks. This approach has been extende…
View article: VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification
VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification Open
This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification methods to evaluate the quality and correctness of generated h…
View article: The role of socio-cultural-demographics in long-term haemodialysis survival
The role of socio-cultural-demographics in long-term haemodialysis survival Open
The socio-cultural-demographic attributes of long-term haemodialysis (HD) treatment survival is of interest. Hence, 129 adult chronic kidney disease patients, who have been on maintenance haemodialysis for >3 years, were evaluated for soci…
View article: The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature
The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature Open
View article: Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference Open
As they become more capable, large language models (LLMs) have continued to rapidly increase in size. This has exacerbated the difficulty in running state of the art LLMs on small, edge devices. Standard techniques advocate solving this pr…
View article: Detecting All-to-One Backdoor Attacks in Black-Box DNNs via Differential Robustness to Noise
Detecting All-to-One Backdoor Attacks in Black-Box DNNs via Differential Robustness to Noise Open
The all-to-one (A2O) backdoor attack is one of the major adversarial threats against neural networks. Most existing A2O backdoor defenses operate in a white-box context, necessitating access to the backdoored model’s architecture, hidden l…
View article: Cost-Effective E-Commerce Catalog Translation at Scale Ensuring Named Entity Protection
Cost-Effective E-Commerce Catalog Translation at Scale Ensuring Named Entity Protection Open
View article: Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics
Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Open
Human perception of similarity across uni- and multimodal inputs is highly complex, making it challenging to develop automated metrics that accurately mimic it. General purpose vision-language models, such as CLIP and large multi-modal mod…
View article: Out-of-Distribution Detection with Overlap Index
Out-of-Distribution Detection with Overlap Index Open
Out-of-distribution (OOD) detection is crucial for the deployment of machine learning models in the open world. While existing OOD detectors are effective in identifying OOD samples that deviate significantly from in-distribution (ID) data…
View article: PrefixLLM: LLM-aided Prefix Circuit Design
PrefixLLM: LLM-aided Prefix Circuit Design Open
Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for enhancing the perf…
View article: TruncFormer: Private LLM Inference Using Only Truncations
TruncFormer: Private LLM Inference Using Only Truncations Open
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains practically intractable due to the massive latency costs a…
View article: C2HLSC: Leveraging Large Language Models to Bridge the Software-to-Hardware Design Gap
C2HLSC: Leveraging Large Language Models to Bridge the Software-to-Hardware Design Gap Open
High-Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for automatically refactoring C code into HLS-compatible …
View article: Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI
Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI Open
Masala-CHAI is a fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a long-standing challenge in circuit design automation: auto…
View article: Automatically Improving LLM-based Verilog Generation using EDA Tool Feedback
Automatically Improving LLM-based Verilog Generation using EDA Tool Feedback Open
Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are eme…
View article: ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search Open
View article: SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs
SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs Open
Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the context of applications like cloud computing, ZKPs can be used by a client (called the verifier) to verify the service provider (called the prover) is in…
View article: EMMA: Efficient Visual Alignment in Multi-Modal LLMs
EMMA: Efficient Visual Alignment in Multi-Modal LLMs Open
Multi-modal Large Language Models (MLLMs) have recently exhibited impressive general-purpose capabilities by leveraging vision foundation models to encode the core concepts of images into representations. These are then combined with instr…
View article: Rome was Not Built in a Single Step: Hierarchical Prompting for LLM-based Chip Design
Rome was Not Built in a Single Step: Hierarchical Prompting for LLM-based Chip Design Open
Large Language Models (LLMs) are effective in computer hardware synthesis via hardware description language (HDL) generation. However, LLM-assisted approaches for HDL generation struggle when handling complex tasks. We introduce a suite of…
View article: ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search Open
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 …
View article: LLM-Aided Testbench Generation and Bug Detection for Finite-State Machines
LLM-Aided Testbench Generation and Bug Detection for Finite-State Machines Open
This work investigates the potential of tailoring Large Language Models (LLMs), specifically GPT3.5 and GPT4, for the domain of chip testing. A key aspect of chip design is functional testing, which relies on testbenches to evaluate the fu…
View article: C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?
C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap? Open
High Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for refactoring C code into HLS-compatible formats. We pr…
View article: NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security Open
Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoroughly evaluated. To address this, we develop a novel method…
View article: Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing
Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing Open
The rapid advancement of large language models (LLMs) has significantly improved code completion tasks, yet the trade-off between accuracy and computational cost remains a critical challenge. While using larger models and incorporating inf…
View article: Learning-Based Compress-and-Forward Schemes for the Relay Channel
Learning-Based Compress-and-Forward Schemes for the Relay Channel Open
The relay channel, consisting of a source-destination pair along with a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including …
View article: Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM
Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM Open
High peak-to-average power ratio (PAPR) is one of the main factors limiting cell coverage for cellular systems, especially in the uplink direction. Discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM) wit…