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Bit-level sparsity methods skip ineffectual zero-bit operations and are typically applicable within bit-serial deep learning accelerators. This type of sparsity at the bit-level is especially interesting because it is both orthogonal and compatible with other deep neural network (DNN) efficiency methods such as quantization and pruning. In this work, we improve the practicality and efficiency of bitlevel sparsity through a novel algorithmic bit-pruning, averaging, and compression method, and a co-designed efficient bit-serial hardware accelerator. On the algorithmic side, we introduce bidirectional bit sparsity (BBS). The key insight of BBS is that we can leverage bit sparsity in a symmetrical way to prune either zero-bits or one-bits. This significantly improves the load balance of bit-serial computing and guarantees the level of sparsity to be more than 50%. On top of BBS, we further propose two bit-level binary pruning methods that require no retraining, and can be seamlessly applied to quantized DNNs. Combining binary pruning with a new tensor encoding scheme, BBS can both skip computation and reduce the memory footprint associated with bi-directional sparse bit columns. On the hardware side, we demonstrate the potential of BBS through BitVert, a bitserial architecture with an efficient PE design to accelerate DNNs with low overhead, exploiting our proposed binary pruning. Evaluation on seven representative DNN models shows that our approach achieves: (1) on average 1.66$\times$ reduction in model sizewith negligible accuracy loss of < 0.5%; (2) up to 3.03$\times$ speedupand 2.44$\times$ energy saving compared to prior DNN accelerators., Comment: Accepted by IEEE/ACM MICRO 2024

FPGAs offer a flexible platform for accelerating deep neural network (DNN) inference, particularly for non-uniform workloads featuring fine-grained unstructured sparsity and mixed arithmetic precision. To leverage these redundancies, an emerging approach involves partially or fully unrolling computations for each DNN layer. That way, parameter-level and bit-level ineffectual operations can be completely skipped, thus saving the associated area and power. Regardless, unrolled implementations scale poorly and limit the size of a DNN that can be unrolled on an FPGA. This motivates the investigation of new reconfigurable architectures to improve the efficiency of unrolled DNNs, while taking advantage of sparsity and mixed precision. To enable this, we present Kratos: a focused FPGA benchmark of unrolled DNN primitives with varying levels of sparsity and different arithmetic precisions. Our analysis reveals that unrolled DNNs can operate at very high frequencies, reaching the maximum frequency limit of an Arria 10 device. Additionally, we found that substantial area reductions can be achieved through fine-grained sparsity and low bit-width. We build on those results to tailor the FPGA fabric for unrolled DNNs through an architectural case study demonstrating $\sim$2$\times$ area reduction when using smaller LUT sizes within current FPGAs. This paves the way for further exploration of new programmable architectures that are purpose-built for sparse and low-precision unrolled DNNs. Our source code and benchmark are available on github.com/abdelfattah-lab/Kratos-benchmark., Comment: accepted at FPL 2024

The increasing size of large language models (LLMs) traditionally requires low-precision integer formats to meet strict latency and power demands. Yet recently, alternative formats such as Normal Float (NF4) have increased model accuracy at the cost of increased chip area. In this work, we first conduct a large-scale analysis of LLM weights and activations across 30 networks and conclude that most distributions follow a Student's t-distribution. We then derive a new theoretically optimal format, Student Float (SF4), that improves over NF4 across modern LLMs, for example increasing the average accuracy on LLaMA2-7B by 0.76% across tasks. Using this format as a high-accuracy reference, we then propose augmenting E2M1 with two variants of supernormal support for higher model accuracy. Finally, we explore the quality and efficiency frontier across 11 datatypes by evaluating their model accuracy and hardware complexity. We discover a Pareto curve composed of INT4, E2M1, and E2M1 with supernormal support, which offers a continuous tradeoff between model accuracy and chip area. For example, E2M1 with supernormal support increases the accuracy of Phi-2 by up to 2.19% with 1.22% area overhead, enabling more LLM-based applications to be run at four bits. The supporting code is hosted at https://github.com/cornell-zhang/llm-datatypes., Comment: Accepted to ICML 2024

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

Mixed-precision quantization is a popular approach for compressing deep neural networks (DNNs). However, it is challenging to scale the performance efficiently with mixed-precision DNNs given the current FPGA architecture and conventional accelerator dataflows. In this work, we enhance the FPGA's capability for accelerating mixed-precision DNNs by proposing M4BRAM, a novel compute-in-block RAM (BRAM) architecture that can compute mixed-precision matrix-matrix multiplication. On the precision side, M4BRAM supports a wide range of mixed-precision DNN configurations -- the weight precision can be 2/4/8 bits while the activation precision can vary from 2 to 8 bits. On the dataflow side, M4BRAM leverages a novel in-BRAM data duplication scheme to achieve high hardware utilization. Moreover, during M4BRAM computation, other FPGA resources can seamlessly access its data without the need for a separate buffer. Hence, unlike prior compute-in-BRAM proposals, M4BRAM can simultaneously perform mixed-precision computation and maintain full functionality as a memory unit to \textit{truly} complement the existing compute resources on FPGAs. Experiments show that adding M4BRAM to a tiled DNN accelerator can achieve an average speedup of 2.16$\times$ across various DNNs on the ImageNet classification task while incurring a negligible accuracy loss of $<$ 0.5%. Compared to the same tiled accelerator that employs a prior compute-in-BRAM architecture, M4BRAM delivers 1.43$\times$ higher performance on average across various DNNs., Comment: 10 pages, 12 figures, 3 tables, IEEE ICFPT 2023

Deep neural network (DNN) inference using reduced integer precision has been shown to achieve significant improvements in memory utilization and compute throughput with little or no accuracy loss compared to full-precision floating-point. Modern FPGA-based DNN inference relies heavily on the on-chip block RAM (BRAM) for model storage and the digital signal processing (DSP) unit for implementing the multiply-accumulate (MAC) operation, a fundamental DNN primitive. In this paper, we enhance the existing BRAM to also compute MAC by proposing BRAMAC (Compute-in-$\underline{\text{BR}}$AM $\underline{\text{A}}$rchitectures for $\underline{\text{M}}$ultiply-$\underline{\text{Ac}}$cumulate). BRAMAC supports 2's complement 2- to 8-bit MAC in a small dummy BRAM array using a hybrid bit-serial & bit-parallel data flow. Unlike previous compute-in-BRAM architectures, BRAMAC allows read/write access to the main BRAM array while computing in the dummy BRAM array, enabling both persistent and tiling-based DNN inference. We explore two BRAMAC variants: BRAMAC-2SA (with 2 synchronous dummy arrays) and BRAMAC-1DA (with 1 double-pumped dummy array). BRAMAC-2SA/BRAMAC-1DA can boost the peak MAC throughput of a large Arria-10 FPGA by 2.6$\times$/2.1$\times$, 2.3$\times$/2.0$\times$, and 1.9$\times$/1.7$\times$ for 2-bit, 4-bit, and 8-bit precisions, respectively at the cost of 6.8%/3.4% increase in the FPGA core area. By adding BRAMAC-2SA/BRAMAC-1DA to a state-of-the-art tiling-based DNN accelerator, an average speedup of 2.05$\times$/1.7$\times$ and 1.33$\times$/1.52$\times$ can be achieved for AlexNet and ResNet-34, respectively across different model precisions., Comment: 11 pages, 13 figures, 3 tables, FCCM conference 2023

Unsupervised clustering has broad applications in data stratification, pattern investigation and new discovery beyond existing knowledge. In particular, clustering of bioactive molecules facilitates chemical space mapping, structure-activity studies, and drug discovery. These tasks, conventionally conducted by similarity-based methods, are complicated by data complexity and diversity. We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1.39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space. These band-clusters, displayed by a space-navigation simulation software, band molecules of selected bioactivity classes into individual band-clusters possessing unique sets of common sub-structural features beyond structural similarity. These sub-structural features form the frameworks of the literature-reported pharmacophores and privileged fragments. Within each band-cluster, molecules are further banded into selected sub-regions with respect to their bioactivity target, sub-structural features and molecular scaffolds. Our method is potentially applicable for big data clustering tasks of different fields.