Image sensors have low costs and broad applications, but the large data volume they generate can result in significant energy and latency overheads during data transfer, storage, and processing.This paper explores how shifting from traditional binary encoding to delay-based codes early on can address these inefficiencies, enabling keypoint detection and tracking within digital pixel sensors.The result is SEAL, a Single-Event Architecture for In-Sensor Localization.SEAL optimizes the entire pipeline between the pixel array and the sensor-processor interface by introducing a temporal processor co-designed with analog-to-time converters, followed by a custom heavily quantized frontend processor.Its implementation is fully digital, relying on off-the-shelf CMOS cells and EDA tools, and adheres to race logic's single-wire-per-variable and single-eventper-wire policies to maximize energy and area efficiency wherever possible.Our evaluation-combining analog and digital simulations, FPGA prototyping, and an end-to-end system analysis incorporating a host processor for visual inertial odometry (VIO) backend tasks-demonstrates a 16-61× reduction in the latency of keypoint detection and tracking compared to software baselines running on the host processor, and a 7× reduction in energy consumption compared to a standard digital pixel sensor without processing capabilities.Meanwhile, SEAL preserves robust tracking accuracy: on the EuRoC dataset, the average root mean square absolute trajectory error decreases by 1.0 cm for HybVIO and increases by just 0.3 cm for VINS-Mono compared to their original implementations.

Gate-level clocking, typical in traditional approaches to Single Flux Quantum (SFQ) technology, makes the effective synthesis of superconducting circuits a significant engineering hurdle. This paper addresses this challenge by employing the recently introduced alternating SFQ (xSFQ) logic family. xSFQ leverages dual-rail alternating encoding to eliminate the clock dependency from the superconducting gate semantics. This obviates the need for ad hoc modifications to existing synthesis tools and avoids unnecessary circuit resource overheads, marking a significant advancement in superconducting circuit design automation. Our implementation results demonstrate an average reduction of over 80% in the Josephson junction count for circuits from the ISCAS85, EPFL, and ISCAS89 benchmark suites.

Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design challenges that hinder their practicality. In this work, we conduct a holistic exploration of printed neural network accelerators, starting from the analog-to-digital interface---a major area and power sink for sensor processing applications---and extending to networks of ternary neurons and their implementation. We propose bespoke ternary neural networks using approximate popcount and popcount-compare units, developed through a multi-phase evolutionary optimization approach and interfaced with sensors via customizable analog-to-binary converters. Our evaluation results show that the presented designs outperform the state of the art, achieving at least 6× improvement in area and 19× in power. To our knowledge, they represent the first open-source digital printed neural network classifiers capable of operating with existing printed energy harvesters.

Superconductor electronics (SCE) run at hundreds of GHz and consume only a fraction of the dynamic power of CMOS, but are naturally pulse-based, and operate on impulses with picosecond widths. The transiency of these operations necessitates using logic cells that are inherently stateful. Adopting stateful gates, however, implies an entire reconstruction of the design, simulation, and verification stack. Though challenging, this unique opportunity allows us to build a design framework from the ground up using fundamental principles of programming language design. To this end, we propose PyLSE, an embedded pulse-transfer level language for superconductor electronics. We define PyLSE through formal semantics based on transition systems, and build a framework around them to simulate and analyze SCE cells digitally. To demonstrate its features, we verify its results by model checking in UPPAAL, and compare its complexity and timing against a set of cells designed as analog circuit schematics and simulated in Cadence.

K-means is one of the most important unsuper-vised learning algorithms. In this paper, we present TiAcc, a triangle-inequality based K-means hardware accelerator on FPGAs. TiAcc highlights itself with an algorithm-hardware co-design strategy tailored for K-means clustering. Specifically, TiAcc leverages a novel triangle-inequality based filtering to eliminate unnecessary distance computations without changing the final clustering results. Meanwhile, it employs a pipeline decoupling approach to mitigate the irregularity of the remaining computations, and an efficient hardware architecture design to fully exploit the pipeline and parallel processing capability of FPGAs. Moreover, TiAcc provides parameterized configuration knobs that can minimize the manual efforts in the arduous hardware design process and provides flexibility to optimize hardware designs for a variety of datasets with different sizes and dimensionalities. Intensive experiments show that TiAcc achieves an average 4.94× speedup and significant energy efficiency (average 74.22 ×) compared with an optimized K-means running on a server-grade Xeon CPU.

Although superconducting single flux quantum (SFQ) technologies offer the potential for low-latency operation with energy dissipation of the order of attojoules per gate, their inherently pulse-driven nature and stateful cells have led to designs in which every logic gate is clocked. This means that clocked buffers must be added to equalize logic path lengths, and every gate becomes a pipeline stage. We propose a different approach, where gates are clock-free and synchronous designs have a conventional look-and-feel. Despite being clock-free, however, the gates are state machines by nature. To properly manage these state machines, the logical clock cycle is composed of two synchronous alternating phases: the first of which implements the desired function, and the second of which returns the state machines to the ground state. Moreover, to address the challenges associated with the asynchronous implementation of Boolean NOT operations in pulse-based systems, values are represented as unordered binary codes – in particular, dual-rail codes. With unordered codes, AND and OR operations are functionally complete.We demonstrate that our new approach, xSFQ, with its dual-rail construction and alternating clock phases, along with "double-pumped" logical latches and a timing optimization through latch decomposition, is capable of implementing arbitrary digital designs without gate-level pipelining and the overheads that come with it. We evaluate energy-delay trade-offs enabled by this approach through a mix of detailed analog circuit modeling, pulse-level discrete-event simulation, and high-level pipeline efficiency analysis. The resulting systems are shown to deliver energy-delay product (EDP) gains over conventional SFQ even with pipeline hazard ratios (HR) below 1%. For hazard ratios equal to 15% and 20% and a design resembling a RISC-V RV32I core (excluding the cost of interlock logic), xSFQ achieves 22x and 31x EDP savings, respectively.

Superconducting logic offers the potential to perform computation at tremendous speeds and energy savings. However, a "semantic gap" lies between the level-driven logic that traditional hardware designs accept as a foundation and the pulse-driven logic that is naturally supported by the most compelling superconducting technologies. A pulse, unlike a level signal, will fire through a channel for only an instant. Arranging the network of superconducting components so that input pulses always arrive simultaneously to "logic gates'' to maintain the illusion of Boolean-only evaluation is a significant engineering hurdle. In this paper, we explore computing in a new and more native tongue for superconducting logic: time of arrival. Building on recent work in delay-based computations we show that superconducting logic can naturally compute directly over temporal relationships between pulse arrivals, that the computational relationships between those pulse arrivals can be formalized through a functional extension to a temporal predicate logic used in the verification community, and that the resulting architectures can operate asynchronously and describe real and useful computations. We verify our hypothesis through a combination of detailed analog circuit models, a formal analysis of our abstractions, and an evaluation in the context of several superconducting accelerators.

As computer architecture continues to expand beyond software-agnostic microarchitecture to specialized and heterogeneous logic or even radically different emerging computing models (e.g., quantum cores, DNA storage units), detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, frequency) calls for a more integrative but higher-level approach. We propose Charm, a modeling language supporting closed-form high-level architecture modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated, reused, and shared. The language is interpreted through a combination of automatic symbolic evaluation, scalable graph transformation, and efficient compiler techniques, generating executable DAGs and optimized analysis procedures. Charm also exploits the advancements in satisfiability modulo theory solvers to automatically search the design space to help architects explore multiple design knobs simultaneously (e.g., different CNN tiling configurations). Through two case studies, we demonstrate that Charm allows one to define high-level architecture models in a clean and concise format, maximize reusability and shareability, capture unreasonable assumptions, and significantly ease design space exploration at a high level.

When extremely low-energy processing is required, the choice of data representation makes a tremendous difference. Each representation (e.g. frequency domain, residue coded, log-scale) comes with a unique set of trade-offs --- some operations are easier in that domain while others are harder. We demonstrate that race logic, in which temporally coded signals are getting processed in a dataflow fashion, provides interesting new capabilities for in-sensor processing applications. Specifically, with an extended set of race logic operations, we show that tree-based classifiers can be naturally encoded, and that common classification tasks can be implemented efficiently as a programmable accelerator in this class of logic. To verify this hypothesis, we design several race logic implementations of ensemble learners, compare them against state-of-the-art classifiers, and conduct an architectural design space exploration. Our proof-of-concept architecture, consisting of 1,000 reconfigurable Race Trees of depth 6, will process 15.2M frames/s, dissipating 613mW in 14nm CMOS.

As computer architecture continues to expand beyond software-agnostic microarchitecture to data center organization, reconfigurable logic, heterogeneous systems, application-specific logic, and even radically different technologies such as quantum computing, detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, cost, voltage, frequency, cooling energy, leakage, etc.) calls for a more integrative but higher-level approach. We propose Charm, a domain specific language supporting Closed-form High-level ARchitecture Modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated and reused. The language is interpreted through a combination of symbolic evaluation (e.g., restructuring) and compiler techniques (e.g., memoization and invariant hoisting), generating executable evaluation functions and optimized analysis procedures. Further supporting reuse, a type system constrains architectural quantities and ensures models operate only in a validated domain. Through two case studies, we demonstrate that Charm allows one to define high-level architecture models concisely, maximize reusability, capture unreasonable assumptions and inputs, and significantly speedup design space exploration.

We introduce PyRTL, a Python embedded hardware design language that helps concisely and precisely describe digital hardware structures. Rather than attempt to infer a good design via HLS, PyRTL provides a wrapper over a well-defined "core" set of primitives in a way that empowers digital hardware design teaching and research. The proposed system takes advantage of the programming language features of Python to allow interesting design patterns to be expressed succinctly, and encourage the rapid generation of tooling and transforms over a custom intermediate representation. We describe PyRTL as a language, its core semantics, the transform generation interface, and explore its application to several different design patterns and analysis tools. Also, we demonstrate the integration of PyRTL-generated hardware overlays into Xilinx PYNQ platform. The resulting system provides an almost "pure" pythonic experience for the prototyping and evaluation of FPGA-based SoCs.

FPGAs have evolved rapidly over the past decade with a significant increase in size and complexity. Today's FPGA is indeed a system-on-chip (SOC) with a wide variety of hardened functionality, multiple asynchronous clock domains and widening interactions among different parts of the chip. It is commonplace to find applications that transfer data and control across clock-domains. This growing complexity, coupled with shrinking design cycles, has turned debug and verification into a critical design concern. Although software analysis tools have made significant progress over the years, this is not the case for debugging applications running on FPGAs. This paper describes a framework that enables non-intrusive application hardware debugging in the presence of multiple asynchronous clock domains. It offers full observability at minimal area and performance cost. The proposed approach uses Readback to retrieve the entire state of the circuit, a feature available in one form or another, in most commercial FPGAs today. Features such as single stepping and waveform reconstruction offer the same level of debug capability for applications accelerated on FPGAs, as simulation tools do for RTL implementations.

Low Density Parity Check(LDPC) codes are a special class of error correction codes widely used in communication and disk storage systems, due to their Shannon limit approaching performance and their favorable structure. In this paper, a methodology for optimized hardware multiplication by constant matrices in GF(2) is introduced and then applied to the Quasi-Cyclic LDPC encoding algorithm. Taking advantage of the fact that the parity check matrix rarely changes, the signals in many cases are hard-wired into the LUTs and thus the cyclic-shifters and block-memories conventionally used are eliminated. Therefore, the proposed framework leads to less complex, mapped to reconfigurable logic designs, whereas it combines the performance of hard-wired solutions (high throughput, low latency) and the flexibility of the software and its hardware counterparts. These advantages in terms of hardware savings and throughput prove that the proposed encoder scheme is suitable for high-speed applications, such as long-haul optical transmission, where speed and resources utilization are a major issue.