Lattice ispMACH 4A系列:高性能可编程逻辑器件

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ispMACH 4A系列是Lattice公司提供的高性能E2CMOS工艺的CPLD(复杂可编程逻辑器件)解决方案,其特点是架构灵活、易于使用且提供可靠的硅产品和软件工具。这款CPLD适用于快速逻辑设计,确保了用户在开发过程中的稳定性和效率。 ispM4A5-12864-10YI是该系列中的一员,它提供了从32到512个宏观单元的密度选择,同时实现100%的利用率和100%的引脚保留,支持5V或3.3V工作电压。这一型号的特点包括: 1. **卓越的一次到位(First-Time-Fit)和重构(re-fit)特性**:设计更改后无需重新布局,方便系统集成,确保了在设计变化后的无缝对接。 2. **速度锁定(SpeedLocking)技术**:通过使用最多20个产品术语(product terms)来驱动输出,ispM4A5能够提供高达5.0ns的典型延迟时间(tPD)和182MHz的最大频率(fCNT),从而确保了固定时序性能的稳定性。 3. **高速接口**:无论是商业级(5.0ns tPD)还是工业级(7.5ns tPD),以及182MHz的极限频率,ispM4A5表现出极高的数据传输速度。 4. **丰富的功能集**:包括多样的设计风格支持,如D/T寄存器和触发器、同步或异步模式、专用输入寄存器、可编程极性、重置/预置信号交换等,提供了极大的设计灵活性。 5. **高级系统集成特性**:ispM4A5兼容3.3V和5V的JEDEC标准操作,支持JTAG(IEEE 1149.1)进行边界扫描测试,便于自动化设备的测试。此外,它还具备5V和3.3V的JTAG内系统编程能力,符合PCI标准,并能在混合供电电压系统设计中安全运行。 6. **封装选项**:ispM4A5提供多种封装类型,如PLCC、PQFP、TQFP、BGA、FBGA和cBGA,满足不同应用对引脚数和封装需求。 ispM4A5-12864-10YI是一款高度灵活且性能优越的CPLD,适合各种高性能应用,通过强大的功能和广泛的兼容性,极大地简化了用户的设计流程,降低了成本并加快了产品的上市时间。

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ,建立了什么模型

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