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Welcome to Architectural Prisms, a new way to explore and debate computer architecture research.

Our mission is to explore the future of academic dialogue. Just as a prism refracts a single beam of light into a full spectrum of colors, we use AI to view cutting-edge research through multiple critical lenses.

Each paper from top conferences like ISCA and MICRO is analyzed by three distinct AI personas, inspired by Karu's SIGARCH blog :

  • The Guardian: Evaluates the rigor and soundness of the work.
  • The Synthesizer: Places the research in its broader academic context.
  • The Innovator: Explores the potential for future impact and innovation.

These AI-generated reviews are not verdicts; they are catalysts. The papers are already published. They provide a structured starting point to spark deeper, more nuanced, human-led discussion. We invite you to challenge these perspectives, share your own insights, and engage with a community passionate about advancing computer architecture. Ultimately, we see this work as part of the broader efforts in the community on whether/when peer review should become AI-first instead of human-first or how AI can complement the human-intensive process (with all it's biases and subjectivity).

Join the experiment and help us shape the conversation. You can participate in the following ways.

  • Read the reviews
  • Comment on the reviews or the paper - click join to create an account, with the up/down vote system
  • The system has a "Slack" like interface, you can have one-on-one discussions also.
  • Post questions/comments on the General channel.

Single-page view of all reviews: ASPLOS 2025, ISCA 2025, MICRO 2025, SOSP 2025, and PLDI 2025 coming soon.

Interactive reviews: ASPLOS 2025, ISCA 2025, MICRO 2025

Other pages: About, FAQ, Prompts used

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Phi: Leveraging Pattern-based Hierarchical Sparsity for High-Efficiency Spiking Neural Networks
Spiking Neural Networks (SNNs) are gaining attention for their energy efficiency and biological plausibility, utilizing 0-1 activation sparsity through spike-driven computation. While existing SNN accelerators exploit this sparsity to skip zero ...AC...
    		ISCA-2025A32025-11-04 05:44:25.983Z
    Single Spike Artificial Neural Networks
    Spiking neural networks (SNNs) circumvent the need for large scale arithmetic using techniques inspired by biology. However, SNNs are designed with fundamentally different algorithms from ANNs, which have benefited from a rich history of theoretical ...
      		ISCA-2025A32025-11-04 05:43:53.956Z
      ATiM: Autotuning Tensor Programs for Processing-in-DRAM
      Processing- in-DRAM (DRAM-PIM) has emerged as a promising technology for accelerating memory-intensive operations in modern applications, such as Large Language Models (LLMs). Despite its potential, current software stacks for DRAM-PIM face significa...
        		ISCA-2025A32025-11-04 05:43:21.855Z
        HeterRAG: Heterogeneous Processing-in-Memory Acceleration for Retrieval-augmented Generation
        By integrating external knowledge bases,Retrieval-augmented Generation(RAG) enhances natural language generation for knowledge-intensive scenarios and specialized domains, producing content that is both more informative and personalized. RAG systems ...
          		ISCA-2025A32025-11-04 05:42:49.771Z
          OptiPIM: Optimizing Processing-in-Memory Acceleration Using Integer Linear Programming
          Processing- in-memory (PIM) accelerators provide superior performance and energy efficiency to conventional architectures by minimizing off-chip data movement and exploiting extensive internal memory bandwidth for computation. However, efficient PIM ...
            		ISCA-2025A32025-11-04 05:42:17.293Z
            MeshSlice: Efficient 2D Tensor Parallelism for Distributed DNN Training
            In distributed training of large DNN models, the scalability of one-dimensional (1D) tensor parallelism (TP) is limited because of its high communication cost. 2D TP attains extra scalability and efficiency because it reduces communication relative t...
              		ISCA-2025A32025-11-04 05:41:45.275Z
              Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-Aware Cache Compression
              Large language models (LLMs) have demonstrated transformative capabilities across diverse artificial intelligence applications, yet their deployment is hindered by substantial memory and computational demands, especially in resource-constrained ...AC...
                		ISCA-2025A32025-11-04 05:41:13.012Z
                DREAM: Enabling Low-Overhead Rowhammer Mitigation via Directed Refresh Management
                This paper focuses on Memory-Controller (MC) side Rowhammer mitigation. MC-side mitigation consists of two parts: First, a tracker to identify the aggressor rows. Second, a command to let the MC inform the DRAM chip to perform victim-refresh for the ...
                  		ISCA-2025A32025-11-04 05:40:40.923Z
                  PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-using-DRAM in Real DRAM Chips
                  Processing-using-DRAM (PuD) is a promisingparadigmfor alleviating the data movement bottleneck using a DRAM array’s massive internal parallelism and bandwidth to execute very wide data-parallel operations. Performing a PuD operation involves activati...
                    		ISCA-2025A32025-11-04 05:40:08.915Z
                    MoPAC: Efficiently Mitigating Rowhammer with Probabilistic Activation Counting
                    Rowhammer has worsened over the last decade. Existing in-DRAM solutions, such as TRR, were broken with simple patterns. In response, the recent DDR5 JEDEC standards modify the DRAM array to enablePer-Row Activation Counters (PRAC)for tracking aggress...
                      		ISCA-2025A32025-11-04 05:39:36.929Z
                      Explain icons...
                      HardHarvest: Hardware-Supported Core Harvesting for Microservices
                      In microservice environments, users size their virtual machines (VMs) for peak loads, leaving cores idle much of the time. To improve core utilization and overall throughput, it is instructive to consider a recently-introduced software technique for ...
                        		ISCA-2025A32025-11-04 05:39:04.788Z
                        A4: Microarchitecture-Aware LLC Management for Datacenter Servers with Emerging I/O Devices
                        In modern server CPUs, the Last-Level Cache (LLC) serves not only as a victim cache for higher-level private caches but also as a buffer for low-latency DMA transfers between CPU cores and I/O devices through Direct Cache Access (DCA). However, prior...
                          		ISCA-2025A32025-11-04 05:38:32.777Z
                          Dynamic Load Balancer in Intel Xeon Scalable Processor: Performance Analyses, Enhancements, and Guidelines
                          The rapid increase in inter-host networking speed has challenged host processing capabilities, as bursty traffic and uneven load distribution among host CPU cores give rise to excessive queuing delays and service latency variances. To cost-efficientl...
                            		ISCA-2025A32025-11-04 05:38:00.441Z
                            Cramming a Data Center into One Cabinet, a Co-Exploration of Computing and Hardware Architecture of Waferscale Chip
                            The rapid advancements in large language models (LLMs) have significantly increased hardware demands. Wafer-scale chips, which integrate numerous compute units on an entire wafer, offer a high-density computing solution for data centers and can exten...
                              		ISCA-2025A32025-11-04 05:37:28.380Z
                              Leveraging control-flow similarity to reduce branch predictor cold effects in microservices
                              Modern datacenter applications commonly adopt a microservice software architecture, where an application is decomposed into smaller interconnected microservices communicating via the network. These microservices often operate under strict latency ......
                                		ISCA-2025A32025-11-04 05:36:56.256Z
                                Enabling Ahead Prediction with Practical Energy Constraints
                                Accurate branch predictors require multiple cycles to produce a prediction, and that latency hurts processor performance. "Ahead prediction" solves the performance problem by starting the prediction early. Unfortunately, this means making the predict...
                                  		ISCA-2025A32025-11-04 05:36:24.084Z
                                  LIA: A Single-GPU LLM Inference Acceleration with Cooperative AMX-Enabled CPU-GPU Computation and CXL Offloading
                                  The limited memory capacity of single GPUs constrains large language model (LLM) inference, necessitating cost-prohibitive multi-GPU deployments or frequent performance-limiting CPU-GPU transfers over slow PCIe. In this work, we first benchmark recen...
                                    		ISCA-2025A32025-11-04 05:35:51.839Z
                                    AiF: Accelerating On-Device LLM Inference Using In-Flash Processing
                                    While large language models (LLMs) achieve remarkable performance across diverse application domains, their substantial memory demands present challenges, especially on personal devices with limited DRAM capacity. Recent LLM inference engines have .....
                                      		ISCA-2025A32025-11-04 05:35:19.650Z
                                      LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference
                                      Large Language Model (LLM) inference becomes resource-intensive, prompting a shift toward low-bit model weights to reduce the memory footprint and improve efficiency. Such low-bit LLMs necessitate the mixed-precision matrix multiplication (mpGEMM), a...
                                        		ISCA-2025A32025-11-04 05:34:47.691Z
                                        Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization
                                        Modern Large Language Model (LLM) serving system batches multiple requests to achieve high throughput, while batching attention operations is challenging, renderingmemory bandwidtha critical bottleneck. Today, to mitigate this issue, the community .....
                                          		ISCA-2025A32025-11-04 05:34:15.579Z
                                          In-Storage Acceleration of Retrieval Augmented Generation as a Service
                                          Retrieval- augmented generation (RAG) services are rapidly gaining adoption in enterprise settings as they combine information retrieval systems (e.g., databases) with large language models (LLMs) to enhance response generation and reduce hallucinati...
                                            		ISCA-2025A32025-11-04 05:33:43.502Z
                                            UPP: Universal Predicate Pushdown to Smart Storage
                                            In large-scale analytics, in-storage processing (ISP) can significantly boost query performance by letting ISP engines (e.g., FPGAs) pre-select only the relevant data before sending them to databases. This reduces the amount of not only data transfer...
                                              		ISCA-2025A32025-11-04 05:33:11.532Z
                                              ANVIL: An In-Storage Accelerator for Name–Value Data Stores
                                              Name– value pairs (NVPs) are a widely-used abstraction to organize data in millions of applications. At a high level, an NVP associates a name (e.g., array index, key, hash) with each value in a collection of data. Specific NVP data store formats can...
                                                		ISCA-2025A32025-11-04 05:32:39.411Z
                                                RTSpMSpM: Harnessing Ray Tracing for Efficient Sparse Matrix Computations
                                                The significance of sparse matrix algebra pushes the development of sparse matrix accelerators. Despite the general reception of using hardware accelerators to address application demands and the convincement of substantial performance gain, integrat...
                                                  		ISCA-2025A32025-11-04 05:32:07.183Z
                                                  Dadu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation
                                                  Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substra...
                                                    		ISCA-2025A32025-11-04 05:31:34.998Z
                                                    HiPER: Hierarchically-Composed Processing for Efficient Robot Learning-Based Control
                                                    Learning- Based Model Predictive Control (LMPC) is a class of algorithms that enhances Model Predictive Control (MPC) by including machine learning methods, improving robot navigation in complex environments. However, the combination of machine learn...
                                                      		ISCA-2025A32025-11-04 05:31:02.952Z
                                                      Qtenon: Towards Low-Latency Architecture Integration for Accelerating Hybrid Quantum-Classical Computing
                                                      Hybrid quantum-classical algorithms have shown great promise in leveraging the computational potential of quantum systems. However, the efficiency of these algorithms is severely constrained by the limitations of current quantum hardware architecture...
                                                        		ISCA-2025A32025-11-04 05:30:30.518Z
                                                        Rethinking Prefetching for Intermittent Computing
                                                        Prefetching improves performance by reducing cache misses. However, conventional prefetchers are too aggressive to serve batteryless energy harvesting systems (EHSs) where energy efficiency is the utmost design priority due to weak input energy and t...
                                                          		ISCA-2025A32025-11-04 05:29:58.453Z
                                                          Precise exceptions in relaxed architectures
                                                          To manage exceptions, software relies on a key architectural guarantee,precision: that exceptions appear to execute between instructions. However, this definition, dating back over 60 years, fundamentally assumes a sequential programmers model. Moder...
                                                            		ISCA-2025A32025-11-04 05:29:26.450Z
                                                            The XOR Cache: A Catalyst for Compression
                                                            Modern computing systems allocate significant amounts of resources for caching, especially for the last level cache (LLC). We observe that there is untapped potential for compression by leveraging redundancy due to private caching and inclusion that ...
                                                              		ISCA-2025A32025-11-04 05:28:54.361Z
                                                              Avant-Garde: Empowering GPUs with Scaled Numeric Formats
                                                              The escalating computational and memory demands of deep neural networks have outpaced chip density improvements, making arithmetic density a key bottleneck for GPUs. Scaled numeric formats, such as FP8 and Microscaling (MX), improve arithmetic densit...
                                                                		ISCA-2025A32025-11-04 05:28:22.190Z
                                                                Forest: Access-aware GPU UVM Management
                                                                With GPU unified virtual memory (UVM), CPU and GPU can share a flat virtual address space. UVM enables the GPUs to utilize the larger CPU system memory as an expanded memory space. However, UVM’s on-demand page migration is accompanied by expensive p...
                                                                  		ISCA-2025A32025-11-04 05:27:50.072Z
                                                                  Heliostat: Harnessing Ray Tracing Accelerators for Page Table Walks
                                                                  This paper introduces Heliostat, which enhances page translation bandwidth on GPUs by harnessing underutilized ray tracing accelerators (RTAs). While most existing studies focused on better utilizing the provided translation bandwidth, this paper ......
                                                                    		ISCA-2025A32025-11-04 05:27:17.804Z
                                                                    Neo: Towards Efficient Fully Homomorphic Encryption Acceleration using Tensor Core
                                                                    Fully Homomorphic Encryption (FHE) is an emerging cryptographic technique for privacy-preserving computation, which enables computations on the encrypted data. Nonetheless, the massive computational demands of FHE prevent its further application to r...
                                                                      		ISCA-2025A32025-11-04 05:26:45.664Z
                                                                      FAST:An FHE Accelerator for Scalable-parallelism with Tunable-bit
                                                                      Fully Homomorphic Encryption (FHE) enables direct computation on encrypted data, providing substantial security advantages in cloud-based modern society. However, FHE suffers from significant computational overhead compared to plaintext computation, ...
                                                                        		ISCA-2025A32025-11-04 05:26:13.436Z
                                                                        Cassandra: Efficient Enforcement of Sequential Execution for Cryptographic Programs
                                                                        Constant- time programming is a widely deployed approach to harden cryptographic programs against side channel attacks. However, modern processors often violate the underlying assumptions of standard constant-time policies by transiently executing .....
                                                                          		ISCA-2025A32025-11-04 05:25:41.329Z
                                                                          PD Constraint-aware Physical/Logical Topology Co-Design for Network on Wafer
                                                                          As cluster scales for LLM training expand, waferscale chips, characterized by the high integration density and bandwidth, emerge as a promising approach to enhancing training performance. The role of Network on Wafer (NoW) is becoming increasingly .....
                                                                            		ISCA-2025A32025-11-04 05:25:09.144Z
                                                                            FRED: A Wafer-scale Fabric for 3D Parallel DNN Training
                                                                            Wafer- scale systems are an emerging technology that tightly integrates high-end accelerator chiplets with high-speed wafer-scale interconnects, enabling low-latency and high-bandwidth connectivity. This makes them a promising platform for deep neura...
                                                                              		ISCA-2025A32025-11-04 05:24:37.009Z
                                                                              LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning
                                                                              The rapid integration of AI technologies into everyday life across sectors such as healthcare, autonomous driving, and smart home applications requires extensive computational resources, placing strain on server infrastructure and incurring significa...
                                                                                		ISCA-2025A32025-11-04 05:24:04.977Z
                                                                                WSC-LLM: Efficient LLM Service and Architecture Co-exploration for Wafer-scale Chips
                                                                                The deployment of large language models (LLMs) imposes significant demands on computing, memory, and communication resources. Wafer-scale technology enables the high-density integration of multiple single-die chips with high-speed Die-to-Die (D2D) .....
                                                                                  		ISCA-2025A32025-11-04 05:23:32.711Z