CIRCUITS AND ARCHITECTURE FOR BIO-INSPIRED AI ACCELERATORS

Technological advances in microelectronics envisioned through Moore’s law have led to powerful processors that can handle complex and computationally intensive tasks. Nonetheless, these advancements through technology scaling have come at an unfavorable cost of significantly larger power consumption, which has posed challenges for data processing centers and computers at scale. Moreover, with the emergence of mobile computing platforms constrained by power and bandwidth for distributed computing, the necessity for more energy-efficient scalable local processing has become more significant. Unconventional Compute-in-Memory architectures such as the analog winner-takes-all associative-memory and the Charge-Injection Device processor have been proposed as alternatives. Unconventional charge-based computation has been employed for neural network accelerators in the past, where impressive energy efficiency per operation has been attained in 1-bit vector-vector multiplications, and in recent work, multi-bit vector-vector multiplications. In the latter, computation was carried out by counting quanta of charge at the thermal noise limit, using packets of about 1000 electrons. These systems are neither analog nor digital in the traditional sense but employ mixed-signal circuits to count the packets of charge and hence we call them Quasi-Digital. By amortizing the energy costs of the mixed-signal encoding/decoding over compute-vectors with many elements, high energy efficiencies can be achieved. In this dissertation, I present a design framework for AI accelerators using scalable compute-in-memory architectures. On the device level, two primitive elements are designed and characterized as target computational technologies: (i) a multilevel non-volatile cell and (ii) a pseudo Dynamic Random-Access Memory (pseudo-DRAM) bit-cell. At the level of circuit description, compute-in-memory crossbars and mixed-signal circuits were designed, allowing seamless connectivity to digital controllers. At the level of data representation, both binary and stochastic-unary coding are used to compute Vector-Vector Multiplications (VMMs) at the array level. Finally, on the architectural level, two AI accelerator for data-center processing and edge computing are discussed. Both designs are scalable multi-core Systems-on-Chip (SoCs), where vector-processor arrays are tiled on a 2-layer Network-on-Chip (NoC), enabling neighbor communication and flexible compute vs. memory trade-off. General purpose Arm/RISCV co-processors provide adequate bootstrapping and system-housekeeping and a high-speed interface fabric facilitates Input/Output to main memory

Designing peptide nanoparticles for efficient brain delivery

The targeted delivery of therapeutic compounds to the brain is arguably the most significant open problem in drug delivery today. Nanoparticles (NPs) based on peptides and designed using the emerging principles of molecular engineering show enormous promise in overcoming many of the barriers to brain delivery faced by NPs made of more traditional materials. However, shortcomings in our understanding of peptide self-assembly and blood–brain barrier (BBB) transport mechanisms pose significant obstacles to progress in this area. In this review, we discuss recent work in engineering peptide nanocarriers for the delivery of therapeutic compounds to the brain, from synthesis, to self-assembly, to in vivo studies, as well as discussing in detail the biological hurdles that a nanoparticle must overcome to reach the brain

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

A Programmable Five Qubit Quantum Computer Using Trapped Atomic Ions

Quantum computers can solve certain problems more efficiently compared to conventional classical methods. In the endeavor to build a quantum computer, several competing platforms have emerged that can implement certain quantum algorithms using a few qubits. However, the demonstrations so far have been done usually by tailoring the hardware to meet the requirements of a particular algorithm implemented for a limited number of instances. Although such proof of principal implementations are important to verify the working of algorithms on a physical system, they further need to have the potential to serve as a general purpose quantum computer allowing the flexibility required for running multiple algorithms and be scaled up to host more qubits. Here we demonstrate a small programmable quantum computer based on five trapped atomic ions each of which serves as a qubit. By optically resolving each ion we can individually address them in order to perform a complete set of single-qubit and fully connected two-qubit quantum gates and alsoperform efficient individual qubit measurements. We implement a computation architecture that accepts an algorithm from a user interface in the form of a standard logic gate sequence and decomposes it into fundamental quantum operations that are native to the hardware using a set of compilation instructions that are defined within the software. These operations are then effected through a pattern of laser pulses that perform coherent rotations on targeted qubits in the chain. The architecture implemented in the experiment therefore gives us unprecedented flexibility in the programming of any quantum algorithm while staying blind to the underlying hardware. As a demonstration we implement the Deutsch-Jozsa and Bernstein-Vazirani algorithms on the five-qubit processor and achieve average success rates of 95 and 90 percent, respectively. We also implement a five-qubit coherent quantum Fourier transform and examine its performance in the period finding and phase estimation protocol. We find fidelities of 84 and 62 percent, respectively. While maintaining the same computation architecture the system can be scaled to more ions using resources that scale favorably (O(N^2)) with the number of qubits N

Coherent dynamics of a hybrid quantum spin-mechanical oscillator system

A fully functional quantum computer must contain at least two important components: a quantum memory for storing and manipulating quantum information and a quantum data bus to securely transfer information between quantum memories. Typically, a quantum memory is composed of a matter system, such as an atom or an electron spin, due to their prolonged quantum coherence. Alternatively, a quantum data bus is typically composed of some propagating degree of freedom, such as a photon, which can retain quantum information over long distances. Therefore, a quantum computer will likely be a hybrid quantum device, consisting of two or more disparate quantum systems. However, there must be a reliable and controllable quantum interface between the memory and bus in order to faithfully interconvert quantum information. The current engineering challenge for quantum computers is scaling the device to large numbers of controllable quantum systems, which will ultimately depend on the choice of the quantum elements and interfaces utilized in the device. In this thesis, we present and characterize a hybrid quantum device comprised of single nitrogen-vacancy (NV) centers embedded in a high quality factor diamond mechanical oscillator. The electron spin of the NV center is a leading candidate for the realization of a quantum memory due to its exceptional quantum coherence times. On the other hand, mechanical oscillators are highly sensitive to a wide variety of external forces, and have the potential to serve as a long-range quantum bus between quantum systems of disparate energy scales. These two elements are interfaced through crystal strain generated by vibrations of the mechanical oscillator. Importantly, a strain interface allows for a scalable architecture, and furthermore, opens the door to integration into a larger quantum network through coupling to an optical interface.There are a few important engineering challenges associated with this device. First, there have been no previous demonstrations of a strain-mediated spin-mechanical interface and hence the system is largely uncharacterized. Second, fabricating high quality diamond mechanical oscillators is difficult due to the robust and chemically inert nature of diamond. Finally, engineering highly coherent NV centers with a coherent optical interface in nanostructured diamond remains an outstanding challenge. In this thesis, we theoretically and experimentally address each of these challenges, and show that with future improvements, this device is suitable for future quantum-enabled applications. First, we theoretically and experimentally demonstrate a dynamic, strain-mediated coupling between the spin and orbital degrees of freedom of the NV center and the driven mechanical motion of a single-crystal diamond cantilever. We employ Ramsey interferometry to demonstrate coherent, mechanical driving of the NV spin evolution. Using this interferometry technique, we present the first demonstration of nanoscale strain imaging, and quantitatively characterize the previously unknown spin-strain coupling constants. Next, we use the driven motion of the cantilever to perform deterministic control of the frequency and polarization dependence of the optical transitions of the NV center. Importantly, this experiment constitutes the first demonstration of on-chip control of both the frequency and polarization state of a single photon produced by a quantum emitter. In the final experiment, we use mechanical driving to engineer a series of spin ``clock" states and demonstrate a significant increase in the spin coherence time of the NV center. We conclude this thesis with a theoretical discussion of prospective applications for this device, including generation of non-classical mechanical states and spin-spin entanglement, as well as an evaluation of the current limitations of our devices, including a possible avenues for improvement to reach the regime of strong spin-phonon coupling

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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