A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Design and Code Optimization for Systems with Next-generation Racetrack Memories

With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market. Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM . This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation. Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators

Multiscale Study of BaTiO3 Nanostructures and Nanocomposites

Advancements in integrated nanoelectronics will continue to require the use of unique materials or systems of materials with diverse functionalities in increasingly confined spaces. Hence, research on finite-dimensional systems strive to unearth and expand the knowledge of fundamental physical properties in certain key materials which exhibit numerous concurrent and exploitable functions. Correspondingly, ferroelectric nanostructures, which particularly display a plethora of complex phenomena, prevalent in countless fields of research, are noteworthy candidates. Presently, however, the assimilation of zero-(0D) and one-dimensional (1D) ferroelectric into micro- or nano-electronics has been lagging, in part due to a lack of applied and fundamental studies but also due to the paucity of synthetic strategies yielding high quality monocrystalline structures. In this work, the problematics of size reduction, which affects many aspects of electronic devices, was addressed. Furthermore, the depolarizing effects associated with finite thickness in ferroelectric nanostructures was investigated in connection with other crucial boundary conditions. The work reported in this dissertation concerned isolated 0D and 1D BaTiO3 nanocrystals and nanocomposites composed of periodic arrays of BaTiO3 nanowires embedded in a matrix formed by another ferroelectric material. A systematic investigation was conducted for those three types of nanostructures from a quantum mechanical and atomistic perspective using both direct-first-principles and first-principles-derived methods. Using first-principles-based calculations, the structural phase sequences in 0D (cubic-to-tetragonal-to-monoclinic-to-rhombohedral) and 1D (cubic-to-tetragonal-to-orthorhombic-to-monoclinic) BaTiO3 nanoparticles revealed differences from that of the bulk and thin film systems. The monoclinic symmetry found in the 0D compounds, and as for the ground-state of 1D systems, were also affected by size effects and tuned by varying parameters related to the depolarizing effect. Strong electromechanical responses characteristic to the monoclinic symmetry, were also found. In addition, by partially screening the uncompensated charges at the surface of the nanodots, a small range existed (∼87% to ∼95% screening) where both the polarization and toroidal moment coexisted within the nanoparticles. Ferroelectric $nanocomposites$ are novel systems that were also examined and were found to exhibit completely original properties not yet observed in either constituents alone. The temperature-dependent properties such as the structural phases and behavior of the polarization within these nanocomposites were obtained. Interesting new features related to flux-closure configurations were discovered. Transitions associated with the cores of electric dipole vortices were correlated to the direction of in-plane polarization. In addition, vortex-antivortex pairs in a peculiar phase-locked configuration were ascertained in these structures. Complementary density-functional theory calculations were also performed for BaTiO3 nanowires with dissociated-water adsorbates as a function of the out-of-plane lattice constant. Topological defects with winding numbers ranging from 1 to -3 were found in the water-covered nanowires. The ground-state was found to be of triclinic symmetry. Ab-initio calculations were also performed for nanocomposites to investigate the electronic properties of the phase-locked configuration. Similarly to the Monte-Carlo simulations, a configuration containing both vortices (not localized in the nanowires though) and antivortices was found to be the ground state. Mastery of nanomaterials requires merging theoretical research with experimental observation, hence a synthesis project was developed to obtain BaTiO3 nano-tubes and wires using direct pore filling of nanoporous templates. The preliminary results suggested the synthesis of polycrystalline nanostructures depend on the template pore surface polarity and size. The results presented in this dissertation suggested that ferroelectric nanostructures continue to be of great fundamental value and may substantially impact advancement in certain technologies. Furthermore, the work on nanocomposites offered a glimpse to the novel functionalities in ferroelectrics