TC-CIM:Empowering Tensor Comprehensions for Computing-In-Memory

Memristor-based, non-von-Neumann architectures performing tensor operations directly in memory are a promising approach to address the ever-increasing demand for energy-efficient, high-throughput hardware accelerators for Machine Learning (ML) inference. A major challenge for the programmability and exploitation of such Computing-In-Memory (CIM) architectures consists in the efficient mapping of tensor operations from high-level ML frameworks to fixed-function hardware blocks implementing in-memory computations. We demonstrate the programmability of memristor-based accelerators with TC-CIM, a fully-automatic, end-to-end compilation flow from Tensor Comprehensions, a mathematical notation for tensor operations, to fixed-function memristor-based hardware blocks. Operations suitable for acceleration are identified using Loop Tactics, a declarative framework to describe computational patterns in a poly-hedral representation. We evaluate our compilation flow on a system-level simulator based on Gem5, incorporating crossbar arrays of memristive devices. Our results show that TC-CIM reliably recognizes tensor operations commonly used in ML workloads across multiple benchmarks in order to offload these operations to the accelerator

PET-to-MLIR:A polyhedral front-end for MLIR

We present PET-to-MLIR, a new tool to enter the MLIR compiler framework from C source. The tool is based on the popular PET and ISL libraries for extracting and manipulating quasi-affine sets and relations, and Loop Tactics, a declarative optimizer. The use of PET brings advanced diagnosis and full support for C by relying on the Clang parser. ISL allows easy manipulation of the polyhedral representation and efficient code generation. Loop Tactics, on the other hand, enable us to detect computational motifs transparently and lift the entry point in MLIR, thus enabling domain-specific optimizations in general-purpose code.We demonstrate our tool using the Polybench/C benchmark suite and show that it can lower most of the benchmarks to the MLIR’s affine dialect successfully. We believe that our tool can benefit research in the compiler community by providing an automatic way to translate C code to the MLIR affine dialect

How functional programming mattered

In 1989 when functional programming was still considered a niche topic, Hughes wrote a visionary paper arguing convincingly ‘why functional programming matters’. More than two decades have passed. Has functional programming really mattered? Our answer is a resounding ‘Yes!’. Functional programming is now at the forefront of a new generation of programming technologies, and enjoying increasing popularity and influence. In this paper, we review the impact of functional programming, focusing on how it has changed the way we may construct programs, the way we may verify programs, and fundamentally the way we may think about programs

Progressive Raising in Multi-level IR

International audienceMulti-level intermediate representations (IR) show great promise for lowering the design costs for domain-specific compilers by providing a reusable, extensible, and non-opinionated framework for expressing domain-specific and high-level abstractions directly in the IR. But, while such frameworks support the progressive lowering of high-level representations to low-level IR, they do not raise in the opposite direction. Thus, the entry point into the compilation pipeline defines the highest level of abstraction for all subsequent transformations, limiting the set of applicable optimizations, in particular for general-purpose languages that are not semantically rich enough to model the required abstractions. We propose Progressive Raising, a complementary approach to the progressive lowering in multi-level IRs that raises from lower to higher-level abstractions to leverage domain-specific transformations for low-level representations. We further introduce Multi-Level Tactics, our declarative approach for progressive raising, implemented on top of the MLIR framework, and demonstrate the progressive raising from affine loop nests specified in a general-purpose language to high-level linear algebra operations. Our raising paths leverage subsequent high-level domain-specific transformations with significant performance improvements

TDO-CIM: Transparent Detection and Offloading for Computation In-memory

Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures have been proposed, compiler support for such architectures is still lagging behind. In this paper, we close this gap by proposing an end-to-end compilation flow for in-memory computing based on the LLVM compiler infrastructure. Starting from sequential code, our approach automatically detects, optimizes, and offloads kernels suitable for in-memory acceleration. We demonstrate our compiler tool-flow on the PolyBench/C benchmark suite and evaluate the benefits of our proposed in-memory architecture simulated in Gem5 by comparing it with a state-of-the-art von Neumann architecture.Comment: Full version of DATE2020 publicatio

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

International audienceMemristor-based, non-von-Neumann architectures performing tensor operations directly in memory are a promising approach to address the ever-increasing demand for energy-efficient, high-throughput hardware accelerators for Machine Learning (ML) inference. A major challenge for the programmability and exploitation of such Computing-In-Memory (CIM) architectures consists in the efficient mapping of tensor operations from high-level ML frameworks to fixed-function hardware blocks implementing in-memory computations. We demonstrate the programmability of memristor-based accelerators with TC-CIM, a fully-automatic, end-to-end compilation flow from Tensor Comprehensions, a mathematical notation for tensor operations, to fixed-function memristor-based hardware blocks. Operations suitable for acceleration are identified using Loop Tactics, a declarative framework to describe computational patterns in a poly-hedral representation. We evaluate our compilation flow on a system-level simulator based on Gem5, incorporating crossbar arrays of memristive devices. Our results show that TC-CIM reliably recognizes tensor operations commonly used in ML workloads across multiple benchmarks in order to offload these operations to the accelerator

Fiat: Deductive Synthesis of Abstract Data Types in a Proof Assistant

We present Fiat, a library for the Coq proof assistant supporting refinement of declarative specifications into efficient functional programs with a high degree of automation. Each refinement process leaves a proof trail, checkable by the normal Coq kernel, justifying its soundness. We focus on the synthesis of abstract data types that package methods with private data. We demonstrate the utility of our framework by applying it to the synthesis of query structures--abstract data types with SQL-like query and insert operations. Fiat includes a library for writing specifications of query structures in SQL-inspired notation, expressing operations over relations (tables) in terms of mathematical sets. This library includes a suite of tactics for automating the refinement of specifications into efficient, correct- by-construction OCaml code. Using these tactics, a programmer can generate such an implementation completely automatically by only specifying the equivalent of SQL indexes, data structures capturing useful views of the abstract data. Throughout we speculate on the new programming modularity possibilities enabled by an automated refinement system with proved-correct rules. “Every block of stone has a statue inside it and it is the task of the sculptor to discover it.”--MichelangeloNational Science Foundation (U.S.) (NSF grant CCF-1253229)United States. Defense Advanced Research Projects Agency (DARPA, agreement number FA8750-12-2- 0293

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial