At the dawn of the 21st century, Mark Weiser envisioned a world where computers would weave themselves into the fabric of everyday life, becoming indistinguishable from it. This prophecy of ubiquitous computing has not only materialized but has evolved beyond Weiser’s initial predictions, particularly with the advent of Tiny Machine Learning (TinyML). TinyML sits at the intersection of machine learning and embedded systems. It is the application of ML algorithms on small, low-power devices, offering powerful AI capabilities like low-latency decision-making, energy efficiency, data privacy, an ..read more

Large Language Models (LLMs) and other foundation models have revolutionized what is possible to implement in software.  On a regular basis, new AI models with ever greater capabilities, such as converting text to video, are rolled out.  This disruption is so striking that new terminology is needed.  We refer to traditional software – the kind that does not call LLMs at runtime – as Plain Old Software (POSW). We call software that exploits LLMs during execution as AI Software (AISW). One key reason we distinguish these two types is that, even though AISW greatly expands the kin ..read more

Brain-computer interfaces (BCIs) connect the brain with computers and machines, forging a link between natural intelligence and artificial intelligence (AI). In doing so, they enable the use of computational platforms to treat debilitating neurological diseases like epilepsy or Parkinson’s, restore brain function lost due to disease or injury, and even augment human cognition to transcend the current limitations of natural intelligence. BCIs also make it possible for humans one day to leverage the power of AI and process the vast information across the entire internet, with only their thought ..read more

Picture generated by Microsoft Designer powered by DALL·E 3   Computations on sparse matrices/tensors in scientific computing, graph processing, and deep learning are one of the earliest and most studied algorithms for hardware acceleration. Such sparse computations are inefficient on general-purpose CPUs and GPUs due to irregular data patterns, which result in low locality and cannot leverage cache hierarchies. The irregularity also leads to load imbalance when parallelizing computations across multiple processing cores/units. Therefore, sparse computations are challenging as it is diff ..read more

As artificial intelligence continues its meteoric rise, the field of computer architecture is evolving to keep pace. Machine learning (ML) promises to transform architectural design and optimization in ways we are only beginning to grasp. To align perspectives on this shift, we recently hosted a virtual workshop on Architecture 2.0 that was open to the community, which had participants from over 60 universities and 20 tech companies, as well as national labs. Architecture 2.0 is a community-driven effort to enable machine learning to minimize human intervention and build more complex, effi ..read more

Image generated by DALL·E through ChatGPT. Heterogeneous Memory: A Journey Toward Performance The heterogeneous memory system is pivotal in today’s computer architecture. By integrating various types of memory for specific tasks and organizing them as a hierarchy, the heterogeneous memory system achieves a balance of performance, capacity, and energy efficiency. However, the scope of heterogeneous memory systems is limited by the cache-coherence domain. The conventional memory system only allows processors to coherently access their own cache and main memory using load and store instructions ..read more

How many times have you tried setting up a virtual machine, container pods or serveless functions in the cloud and wondered how many resources (cores, memory etc.) to configure? In practice, users request more resources than actually end up using. This is a known problem for cloud providers for many years now (analysis from Alibaba and Microsoft Azure), that leads to low resource and cost efficiency. To address this problem, cloud resource management systems try to dynamically scale the amount of resources given across user applications (Autopilot, Madu), to match their actual resource usage ..read more

Program Overview  The Computer Architecture Long-term Mentorship Program (CALM) was launched by some committee members from Computer Architecture Student Association (CASA) in 2021. The program aims to help junior researchers establish and maintain a long-term mentoring relationship with senior researchers from academia and industry. We hope this program will provide additional resources to those new to the computer architecture community, and those seeking advice in their research projects and career developments. CALM was first kicked off in 2021 with a pilot program during the 2022-20 ..read more

  Our executive committee started in July of 2019. Our usual mandate included launching, delivering, and reporting on initiatives based on three pillars in SIGARCH’s mission statement – technical exchange, talent development and recognition, and outreach. These three pillars have a strong emphasis on diversity and inclusion with initiatives that have impacted communities across ACM and beyond. We document these in our annual reports. Our four-year period not only coincided with a pandemic, which is an unusually rare event in mankind’s history, but more importantly the tragic passing of Hu ..read more

Serverless 101 Serverless computing has emerged as a pivotal development and deployment paradigm for cloud computing. According to a recent analyst report, over 50% of companies that use cloud services have adopted serverless computing. Usage of serverless technologies is rapidly expanding, with a CAGR of over 20%. This substantial and growing uptake underscores the transformative potential and increasing acceptance of serverless computing. In serverless, developers program their applications as workflows of stateless functions, which are invoked on-demand in response to user actions (e.g., c ..read more