Gregory Kielian
Gregory Kielian is a lead of a multidisciplinary team at Google Research, bringing the power of transformers and large language models (LLMs) to edge-hardware. His team explores hardware-software co-design of novel ML architectures, datasets, transformer-training techniques, efficient algorithms, as well as exploration into of the realm of EdgeLLM ASICs. By paving the way for LLMs to efficiently operate locally on edge-devices, Kielian's team aims to unlock a new era of possibilities and real-time interaction with locally-intelligent machines.
Authored Publications
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Open Se Cura: First Silicon Results of an Auditable and Transparent Hardware Root of Trust System using Open EDA in 16-nm
Guanchen Tao
Ming-Hung Chen
Bangfei Pan
Kai Yick
Dennis Sylvester
Mehdi Saligane
IEEE Solid-State Circuits Magazine, 16(2024), pp. 58-66
Preview abstract
Hardware Root of Trust (HRoT) is essential for Internet-of-Things (IoT) devices as it provides critical user data protection. However, each novel use case significantly lengthens the development time for an HRoT system. Furthermore, most HRoT solutions are proprietary, and users lack permission to inspect and audit such systems [1-2]. This paper introduces Open Se Cura, which is an open-source framework designed to expedite the implementation of secure and transparent HRoT systems. It utilizes open-source Electronic Design Automation (EDA) tools like OpenROAD [3-4] and OpenFASOC [5-6], along with open-source Process Design Kits (PDKs), to present a transparent and auditable approach to hardware-software co-design platforms. This approach enables fast and trustworthy HRoT system implementation and is made openly available to reproduce its results and security efficacy [7]. Our reference design is showcased through FPGA emulation, and the first measurement results of a silicon implementation in 16nm of Open Se Cura security domain subsets integrated using open-source EDA are presented.
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Open Se Cura: First Silicon Results of an Auditable and Transparent Hardware Root of Trust System using Open EDA in 16-nm
Guanchen Tao
Ming-Hung Chen
Bangfei Pan
Kai Yick
Dennis Sylvester
Mehdi Saligane
IEEE Solid-State Circuits Magazine, 16(2024), pp. 58-66
Preview abstract
Hardware root of trust (HRoT) is essential for IoT devices as it provides critical user data protection. However, each novel use case significantly lengthens the development time for an HRoT system. Furthermore, most HRoT solutions are proprietary, and users lack permission to inspect and audit such systems [1] , [2] . This article introduces Open Se Cura, which is an open source framework designed to expedite the implementation of secure and transparent HRoT systems. The platform grants designers the flexibility to choose their preferred electronic design automation (EDA) tools. They can opt for proprietary EDA solutions or select from open source alternatives, including OpenROAD [3] , [4] , using the OpenFASOC framework [5] , [6] . Additionally, the platform supports the use of open source process design kits (PDKs) to present a transparent and auditable approach to hardware–software co-design. This approach enables fast and trustworthy HRoT system implementation and is openly available to reproduce its results and security efficacy [7] . The extended version of the Open Se Cura reference design is showcased through FPGA emulation and its 22-nm ASIC implementation. We finally present the first measurement results of a 16-nm silicon implementation of selected components from OpenTitan, the security RoT hardware building block of Open Se Cura. This work was integrated using OpenFASOC’s modular flow, which allows one to call for open tools, such as OpenROAD, for physical design and closed tools for the missing open source EDA in 16 nm.
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ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
Shiwei Liu
Guanchen Tao
Yifei Zou
Derek Chow
Zichen Fan
Kauna Lei
Bangfei Pan
Dennis Sylvester
Mehdi Saligane
Arxiv (2024)
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The self-attention mechanism sets transformer-based large language model (LLM) apart from the convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference on silicon is challenging due to the extensively used Softmax in self-attention. Apart from the non-linearity, the low arithmetic intensity greatly reduces the processing parallelism, which becomes the bottleneck especially when dealing with a longer context. To address this challenge, we propose Constant Softmax (ConSmax), a software-hardware co-design as an efficient Softmax alternative. ConSmax employs differentiable normalization parameters to remove the maximum searching and denominator summation in Softmax. It allows for massive parallelization while performing the critical tasks of Softmax. In addition, a scalable ConSmax hardware utilizing a bitwidth-split look-up table (LUT) can produce lossless non-linear operation and support mix-precision computing. It further facilitates efficient LLM inference. Experimental results show that ConSmax achieves a minuscule power consumption of 0.2 mW and area of 0.0008 mm^2 at 1250-MHz working frequency and 16-nm CMOS technology. Compared to state-of-the-art Softmax hardware, ConSmax results in 3.35x power and 2.75x area savings with a comparable accuracy on a GPT-2 model and the WikiText103 dataset.
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Current approaches to Analog Layout Automation
apply ML techniques such as Graph Convolutional Neural
Networks (GCN) to translate netlist to layout. While these ML
approaches have proven to be effective, they lack the powerful
reasoning capabilities, an intuitive human interface, and standard
evaluation benchmarks that have been improving at a rapid de-
velopment pace in Large Language Models (LLMs). The GLayout
framework introduced in this work translates analog layout into
an expressive, technology generic, compact text representation.
Then, an LLM is taught to understand analog layout through
fine-tuning and in-context learning using Retrieval Augmented
Generation (RAG). The LLM is able to successfully layout unseen
circuits based on new information provided in-context. We train
3.8, 7, and 22 Billion parameter quantized LLMs on a dataset
of less than 50 unique circuits, and text documents providing
layout knowledge. The 22B parameter model is tuned in 2 hours
on a single NVIDIA A100 GPU. The open-source evaluation
set is proposed as an automation benchmark for LLM layout
automation tasks, and ranges from 2-transistor circuits to a
∆Σ ADC. The 22B model completes 70% of the tasks in the
evaluation set, and is able to pass DRC and LVS verification on
unseen 4 transistor blocks.
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Reinforcement Learning-Enhanced Cloud-Based Open Source Analog Circuit Generator for Standard and Cryogenic Temperatures in 130-nm and 180-nm OpenPDKs
Ali Hammoud
Anhang Li
Ayushman Tripathi
Wen Tian
Harsh Khandeparkar
Ryan Wans
Boris Murmann
Dennis Sylvester
Mehdi Saligane
Preview abstract
This work introduces an open-source, Process Technology-agnostic framework for hierarchical circuit netlist, layout, and Reinforcement Learning (RL) optimization. The layout, netlist, and optimization python API is fully modular and publicly installable via PyPI. It features a bottom-up hierarchical construction, which allows for complete design reuse across provided PDKs. The modular hierarchy also facilitates parallel circuit design iterations on cloud platforms. To illustrate its capabilities, a two-stage OpAmp with a 5T first-stage, commonsource second-stage, and miller compensation is implemented. We instantiate the OpAmp in two different open-source process design kits (OpenPDKs) using both room-temperature models and cryogenic (4K) models. With a human designed version as the baseline, we leveraged the parameterization capabilities of the framework and applied the RL optimizer to adapt to the power consumption limits suitable for cryogenic applications while maintaining gain and bandwidth performance. Using the modular RL optimization framework we achieve a 6x reduction in power consumption compared to manually designed circuits while maintaining gain to within 2%.
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This paper presents a Multifunctional wearable
sensing system that integrates flexible Laser-Induced-Graphene
(LIG) based sensors and an Open-Source Analog Front-End
(AFE) chip. The LIG sensors are fabricated on polyimide (PI)
Flexible Printed Circuit Board (FPCB) through CO2 infrared
laser direct-write method. The LIG sensors provide repeatable
high-precision temperature sensing, humidity measurement, and
strain detection capabilities. The temperature sensing charac-
terization shows the resistive LIG sensor has a sensitivity of
-0.0493 %/°C, the linear fit R-square factors ≥ 0.9973 across -40
°C to 125 °C. The capacitive humidity sensor achieves a 23.6
times capacitance at 95% relative humidity (RH) compared to
the value observed in a dry environment. Our proposed AFE
chip contains a hybrid folded-cascode Operational Amplifier
(OPAMP) and a Successive Approximation Register Analog-
to-Digital Converter (SAR ADC). Designed using open-source
analog flow and fabricated in GF180 OpenPDK, the AFE chip
serves as a flexible and universal readout platform, adaptable for
various sensing applications. A real-time demonstration of finger
bending detection is performed to validate the functionality.
The multifunctional sensing capability provide by the wearable
system is attractive for personal healthcare application. This
work underscores the integration of the LIG sensors and the
AFE chip, developed using open-source tools which facilitate
rapid and affordable prototyping for a multifunctional flexible
wearable sensing system.
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